• Working group and Diffusion - submit events - new paper - projects - jobs - seminar (...)

  • Geo-Sci-Info



    Since 2002, Median Technologies has been expanding the boundaries of the identification, interpretation, analysis and reporting of imaging data in the medical world. Our core activity is to develop advanced imaging software solutions and platforms for clinical drug development in oncology, diagnostic support, and cancer patient care. Our software solutions improve the management of cancer patients by helping to better identify pathologies, develop and select patient-specific therapies (precision medicine).

    The company employs a highly-qualified team and leverages its scientific, technical, medical, and regulatory expertise to develop innovative medical imaging analysis software based on Artificial Intelligence, cloud computing and big data. We are driven by our core values** that are essential to us. These values define who we are, what we do, the way we do it, and what we, as Median, aspire to**:
    • Leading innovation with purpose
    • Committing to quality in all we do
    • Supporting our customers in achieving their goals
    • Always remembering to put the patient first

    Today, we are a team of 170+ people spread worldwide in the US, Europe and China. Our company is growing in a fulfilling international and multicultural environment.

    In the context of our research and development in artificial intelligence applied to medical imaging, we are looking for: Research Scientist (Ph.D.) "Data Science and Deep Learning applied to MRI", M / F

    Integrated into a multidisciplinary research and development team within the iBiopsy® project, you are a scientist in the research and development of innovative medical imaging solutions using machine learning and other AI methods.

    Medical imaging is one of the fastest growing fields in machine learning. We are looking for an enthusiastic, dynamic, and organized Data Scientist with strong ML experience, excellent communication skills who will thrive at the heart of technological innovation.

    Presentation of activities and main tasks linked to the job
    Position under the supervision of Artificial Intelligence and Data Science Director


    You will apply your AI/ML/Deep Learning knowledge to develop innovative and robust biomarkers using data coming from medical imaging systems such as MRI and CT scanners and any other relevant data sources.

    Your work will involve research and development of innovative machine learning algorithms> Being part of our front-end innovation organization, you will actively scout, keep track of, evaluate, and leverage disruptive technologies, as well as the emergence of new industrial, academic, and technological trends.

    You will work in collaboration with iBiopsy’s software development team as well as clinical science team.

    In addition, you will transfer technology, and share insights and best practices across innovation teams. You will generate intellectual property for the company. You will be expected to author peer reviewed papers, present results at industry/scientific conferences.

    We expect you to build breakthrough AI-enabled imaging solutions relying on cloud computing; applying supervised and unsupervised Machine Learning techniques to create value from the imaging and clinical data databases generated by our medical research and pharmaceutical industry partners. These AI enabled systems and services go beyond image analysis to transform medical practice and drug development.

    Searched profile
    Education: PhD in in Mathematics, Computer Science or related fields

    Main skills and Experience required:

    • Minimum 3 years of relevant work experience in (deep) machine learning
    • Experience with Medical Imaging (MRI required, CT is an asset), image signatures, large scale visual information extraction, features selection
    • Relevant experience with Python, DL frameworks (i.e. Pytorch) and standard packages such as Scikit-learn, Numpy, Scipy, Pandas
    • Experience desired in Semi-Supervised Learning, Self-supervised Learning, Reinforcement Learning, Adversarial methods.
    • Extraction of multimodal feature extraction
    • Author on related research publication / conferences
    • Solid experience with opensource technologies to accelerate innovation

    Required knowledge:
    • In-depth technical knowledge of AI, deep learning and computer vision
    • Strong fundamental knowledge of statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques and dimensionality reduction

    Additional skills:
    • Strong interpersonal, communication and presentation skills as well as ability to work in global team and organize work
    • Fluent in written and oral English

    o Job location: Sophia Antipolis, France
    o Contract: Permanent, Open-Ended
    o Start: As Soon As Possible
    o Offered salary: will depend on candidate’s skills and experience.

    Benefits offered by the company
    o Fulfilling working and living environment
    o Meal vouchers
    o Canteen
    o Health Plan

    Why working with us ?
    o Join an international, multicultural and fast-growing company
    o Be at the heart of innovation



    Depuis 2002, Median Technologies repousse les limites de l'identification, de l'interprétation, de l'analyse et de la communication des données d'imagerie pour le monde médical. Le cœur de notre activité est le développement de logiciels et de plateformes innovants d’imagerie pour les essais cliniques en oncologie, l’aide au diagnostic et le suivi des patients atteints de cancers ; ces logiciels ont pour but d’améliorer la prise en charge des patients souffrant de cancers en aidant à l’identification des pathologies, à la mise au point et à la sélection de thérapies adaptées aux patients (médecine de précision).

    Notre activité est à la convergence de plusieurs disciplines telles que la médecine, l’imagerie médicale et les technologies de l’information. Nos collaborateurs possèdent des expertises scientifiques (intelligence artificielle, sciences des données), techniques (logiciel, cloud computing), médicales, réglementaires, et de business development toutes utilisées dans le développement et la mise sur le marché de nos applications et des services qui leur sont associés.** Dans notre travail quotidien, nous sommes guidés par quatre valeurs, toutes fondamentales pour nous :**
    • Donner du sens à l’innovation technologique
    • Aider nos clients à atteindre leurs objectifs
    • Mettre la qualité au cœur de notre savoir-être et de notre savoir-faire
    • Amélioration de la qualité des soins patient

    Aujourd’hui, nous sommes 170+ personnes réparties dans le monde entier aux États-Unis, en Europe et en Chine. Nous travaillons dans un contexte international et multiculturel particulièrement attractif et épanouissant.

    Dans le cadre de notre recherche et développement en Intelligence Artificielle appliquée à l’imagerie médicale, nous recherchons : un Docteur (PhD) “Science des données et Apprentissage Profond appliqué à l’IRM", H/F

    Intégré dans une équipe de recherche et développement multidisciplinaire au sein du projet iBiopsy®, vous êtes un scientifique spécialiste de l’IA (Machine Learning, Réseaux Profonds et Science des données) pour la recherche et le développement de solutions innovantes d’imagerie médicale.

    L’imagerie médicale est l’un des domaines les plus dynamiques du Machine Learning. Nous recherchons un Scientifique expérimenté passionné, dynamique, et organisé avec une forte expérience en Machine Learning appliqué en imagerie médicale, possédant d’excellentes compétences en communication pour s’épanouir au cœur de l’innovation technologique.

    Présentation des activités et tâches principales associées au poste
    o Poste sous la supervision du directeur de l’Intelligence Artificielle et de la Science des données

    o Responsabilités :

    Vous utiliserez vos connaissances en Intelligence Artificielle (Machine Learning et Deep Learning) pour développer des biomarqueurs solides et innovants, sur la base de données provenant de systèmes d’imagerie médicale tels que IRM et CT scanners en vous aidant de toutes sources de données pertinentes.

    Votre travail impliquera la recherche et le développement d’algorithmes d’apprentissage novateurs. Etant au cœur de l'innovation de notre organisation, vous participerez activement à l’exploration, la veille technologique, l'évaluation et l'exploitation de technologies innovantes, ainsi que l’émergence de nouvelles tendances industrielles, académiques et technologiques.

    Vous travaillerez en collaboration avec l’équipe de développement logiciel ainsi que l’équipe de science clinique d’iBiopsy.

    En outre, vous transmettrez vos connaissances technologiques et partagerez idées et bonnes pratiques entre les équipes. Vous générerez de la propriété intellectuelle pour l'entreprise. Vous rédigerez des articles scientifiques et présenterez des résultats lors de conférences industrielles/scientifiques.

    Nous attendons de vous la participation au développement de solutions d’imagerie innovantes, basées sur l’intelligence artificielle et s’appuyant sur de l’informatique dématérialisé; l’application de techniques supervisées et non supervisées de Machine Learning pour créer de la valeur depuis des bases de données d’images et de données cliniques générées par nos partenaires en recherche médicale et de l’industrie pharmaceutique. Ces systèmes et services basés sur l’intelligence artificielle iront au-delà de l’analyse d’image pour transformer la pratique médicale et le développement de médicaments.

    Profil sollicité
    o Formation : Doctorat en Mathématiques, Science Informatique, ou domaines équivalents.

    o Principales compétences et expériences requises :
    • Minimum 3 ans d’expérience pertinente en (Deep) Machine Learning.
    • Expérience appliquées aux images médicales (IRM essentiel, CT optionnel), signatures d’image, Extraction d’informations visuelles à grande échelle, techniques de sélection.
    • Compréhension des différentes séquences utilisées en IRM abdominales et en connaitre les spécificités
    • Expérience pertinente en Python, DL frameworks (i.e. Pytorch) et packages standard comme Scikit-learn, Numpy, Scipy, Pandas
    • Expérience souhaité en Semi-Supervised Learning, Self-supervised Learning, Reinforcement Learning, Adversarial methods.
    • Extraction de caractéristiques multimodales.
    • Auteur sur des recherches associées (publications/conférences).
    • Solide expérience en technologies OpenSource pour accélérer l’innovation

    o Connaissances requises :
    • Connaissance technique approfondie en IA, Deep Learning et en Vision par ordinateur
    • Solides connaissances fondamentales en traitement de données statistiques, techniques de régression, réseaux de neurones, arbres de décision, classification, reconnaissance de formes, théorie des probabilités, systèmes stochastiques, inférence bayésienne, techniques statistiques et réduction de la dimensionnalité.

    o Compétences additionnelles :
    • Fortes aptitudes relationnelles, de communication et de présentation, ainsi que la capacité à travailler en équipe et d’organisation de son travail
    • Maîtrise de l’anglais oral et écrit

    Eléments du contrat
    o Poste basé à : Sophia-Antipolis, France
    o Type de contrat : CDI
    o Date de début du contrat : au plus tôt
    o Rémunération : à négocier selon profil

    **Avantages offerts par la société **
    o Tickets restaurant
    o Restaurant d’entreprise
    o Mutuelle d’entreprise
    o Cadre épanouissant

    Pourquoi nous rejoindre ?
    o Rejoignez une société internationale, multiculturelle et en pleine croissance
    o Soyez au cœur de l’innovation

  • Packages for data analysis and modelling


    GeomLoss : Geometric Loss functions between sampled measures, images and volumes

    Find all the docs and tutorials of the version 0.2.3 in the read the docs website:

    N.B.: This is still an alpha release! Please send me your feedback: I will polish the user interface, implement Hausdorff divergences, add support for meshes, images, volumes and clean the documentation over the summer of 2020.

    The GeomLoss library provides efficient GPU implementations for:

    Kernel norms (also known as Maximum Mean Discrepancies).

    Hausdorff divergences, which are positive definite generalizations of the ICP loss, analogous to log-likelihoods of Gaussian Mixture Models.

    Unbiased Sinkhorn divergences, which are cheap yet positive definite approximations of Optimal Transport (Wasserstein) costs.

    These loss functions, defined between positive measures, are available through the custom PyTorch layers SamplesLoss, ImagesLoss and VolumesLoss which allow you to work with weighted point clouds (of any dimension), density maps and volumetric segmentation masks. Geometric losses come with three backends each:

    A simple tensorized implementation, for small problems (< 5,000 samples).

    A reference online implementation, with a linear (instead of quadratic) memory footprint, that can be used for finely sampled measures.

    A very fast multiscale code, which uses an octree-like structure for large-scale problems in dimension <= 3.

    GeomLoss is a simple interface for cutting-edge Optimal Transport algorithms. It provides:

    Support for batchwise computations. Linear (instead of quadratic) memory footprint for large problems, relying on the KeOps library for map-reduce operations on the GPU. Fast kernel truncation for small bandwidths, using an octree-based structure. Log-domain stabilization of the Sinkhorn iterations, eliminating numeric overflows for small values of 𝜀 Efficient computation of the gradients, which bypasses the naive backpropagation algorithm. Support for unbalanced Optimal Transport, with a softening of the marginal constraints through a maximum reach parameter. Support for the ε-scaling heuristic in the Sinkhorn loop, with kernel truncation in dimensions 1, 2 and 3. On typical 3D problems, our implementation is 50-100 times faster than the standard SoftAssign/Sinkhorn algorithm.

    Note, however, that SamplesLoss does not implement the Fast Multipole or Fast Gauss transforms. If you are aware of a well-packaged implementation of these algorithms on the GPU, please contact me!

    The divergences implemented here are all symmetric, positive definite and therefore suitable for measure-fitting applications. For positive input measures 𝛼 and 𝛽, our Loss

    functions are such that
    Loss(𝛼,𝛽) = Loss(𝛽,𝛼),
    0 = Loss(𝛼,𝛼) ⩽ Loss(𝛼,𝛽),
    0 = Loss(𝛼,𝛽) ⟺ 𝛼=𝛽.

    GeomLoss can be used in a wide variety of settings, from shape analysis (LDDMM, optimal transport…) to machine learning (kernel methods, GANs…) and image processing. Details and examples are provided below:

    Maths and algorithms PyTorch API Source code Examples

    GeomLoss is licensed under the MIT license.

    Author and Contributors

    Feel free to contact us for any bug report or feature request:

    Jean Feydy Pierre Roussillon (extensions to brain tractograms and normal cycles)

    Related projects

    You may be interested by:

    The KeOps library, which provides efficient CUDA routines for point cloud processing, with full PyTorch support.

    Rémi Flamary and Nicolas Courty’s Python Optimal Transport library, which provides a reference implementation of OT-related methods for small problems.

    Bernhard Schmitzer’s Optimal Transport toolbox, which provides a reference multiscale solver for the OT problem, on the CPU.

  • Geo-Sci-Info

    green geometry.jpg


    © 2016 Ricardo Vega Bravo & Alberto Puime Otín

  • Call for paper Entropy - new books - new papers - preprints


    Introduction to Symplectic Geometry Jean-Louis Koszul -
    (reed) 2019 Springer LINK Video
    Offers a unique and unified overview of symplectic geometry, Highlights the differential properties of symplectic manifolds, Great interest for the emerging field of "Geometric Science of Information”
    This introductory book offers a unique and unified overview of symplectic geometry, highlighting the differential properties of symplectic manifolds. It consists of six chapters: Some Algebra Basics, Symplectic Manifolds, Cotangent Bundles, Symplectic G-spaces, Poisson Manifolds, and A Graded Case, concluding with a discussion of the differential properties of graded symplectic manifolds of dimensions (0,n). It is a useful reference resource for students and researchers interested in geometry, group theory, analysis and differential equations. This book is also inspiring in the emerging field of Geometric Science of Information, in particular the chapter on Symplectic G-spaces, where Jean-Louis Koszul develops Jean-Marie Souriau's tools related to the non-equivariant case of co-adjoint action on Souriau’s moment map through Souriau’s Cocycle, opening the door to Lie Group Machine Learning with Souriau-Fisher metric.
  • Geo-Sci-Info

    The CS-DC put at disposition of the members of the group a videoconference (bbb) system that can be used for:

    meeting for organisation, projects, review panels ... to record and diffuse on-line (streaming) conferences. The acquired video can then be put online and archived in the forum (contact for more information).

    Login: your full name
    Password: send an email to for reservation at least 24h before the conference you will be given the password that you can transmit to other participants.

    Material: it only requires a standard webcam-micro (usual laptop equipment) and to log on the website (a headphone with microphone integrated is recommended).
    Once logged on the website, the e-meeting interface loads and you will be asked to select your microphone, and then you can start your webcam (third button at the top left). You can also upload a pdf file that you manipulate just as in real conference, and that will be seen by all connected participants. The other participants can also share there webcam and microphone.

    visionconf principle.jpg

    If you need Professional video acquisition and post production for your conference, we recommend you the society COM-1film:
    +33 1 1 83 64 58 88
    They were in charge of GSI2015 video, if you want to see their work have a look at the video on

  • LIX Colloquium 2015 conferences, SEE Conference hosted by Ecole Polytechnique.


    The technical program of GSI2015 covers all the main topics and highlights in the domain of “Geometric Science of Information” including Information Geometry Manifolds of structured data/information and their advanced applications. This proceedings consists solely of original research papers that have been carefully peer-reviewed by two or three experts before, and revised before acceptance.
    The GSI15 program includes the renown invited speaker Professor Charles-Michel Marle (UPMC, Université Pierre et Marie Curie, Paris, France) that gives a talk on “Actions of Lie groups and Lie algebras on symplectic and Poisson manifolds”, and three (3) keynote distinguished speakers:
    Professor Marc Arnaudon (Bordeaux University, France): “Stocastic Euler-Poincaré reduction,”
    Professor Tudor Ratiu (EPFL, Switzerland): “Symetry methods in geometric mechanics,”
    Professor Matilde Marcolli (Caltech, US): “From Geometry and Physics to Computational Linguistics”,
    and a short course given by Professor Dominique Spehner (Grenoble University, France) on the “Geometry on the set of quantum states and quantum correlations” chaired by Roger Balian (CEA, France).
    The collection of papers have been arranged into the following seventeen (17) thematic sessions that illustrates the richness and versatility of the field:

    Dimension reduction on Riemannian manifolds, Optimal Transport, Optimal Transport and applications in Imagery/Statistics, Shape Space & Diffeomorphic mappings, Random Geometry & Homology, Hessian Information Geometry, Topological forms and Information, Information Geometry Optimization, Information Geometry in Image Analysis, Divergence Geometry, Optimization on Manifold, Lie Groups and Geometric Mechanics/Thermodynamics, Computational Information Geometry, Lie Groups: Novel Statistical and Computational Frontiers, Geometry of Time Series and Linear Dynamical systems, Bayesian and Information Geometry for Inverse Problems, Probability Density Estimation.

    Historical background
    As for the first edition of GSI (2013) and in past publications, GSI2015 addresses inter-relations between different mathematical domains like shape spaces (geometric statistics on manifolds and Lie groups, deformations in shape space, ...), probability/optimization & algorithms on manifolds (structured matrix manifold, structured data/Information, ...), relational and discrete metric spaces (graph metrics, distance geometry, relational analysis,...), computational and Hessian information geometry, algebraic/infinite dimensional/Banach information manifolds, divergence geometry, tensor-valued morphology, optimal transport theory, manifold & topology learning, ... and applications like geometries of audio-processing, inverse problems and signal processing.
    At the turn of the century, new and fruitful interactions were discovered between several branches of science: Information Science (information theory, digital communications, statistical signal processing,), Mathematics (group theory, geometry and topology, probability, statistics,...) and Physics (geometric mechanics, thermodynamics, statistical physics, quantum mechanics, ...).

    From Probability to Geometry
    Probability is again the subject of a new foundation to apprehend new structures and generalize the theory to more abstract spaces (metric spaces, shape space, homogeneous manifolds, graphs ....). A first attempt to probability generalization in metric spaces was developed by Maurice Fréchet in the middle of last century, in the framework of abstract spaces topologically affine and “distance space” (“espace distancié”). More recently, Misha Gromov, at IHES (Institute of Advanced Scientific Studies), indicates possibilities for (non-)homological linearization of basic notions of the probability theory and also the replacement of the real numbers as values of probabilities by objects of suitable combinatorial categories. In parallel, Daniel Bennequin, from Institut mathématique de Jussieu, observes that entropy is a universal co-homological class in a theory associated to a family of observable quantities and a family of probability distributions.

    From Groups Theory to Geometry
    As observed by Gaston Bachelard, “the group provides evidence of a mathematic closed on itself. Its discovery closes the era of conventions, more or less independent, more or less coherent”. About Elie Cartan’s work on Group Theory, Henri Poincaré said that “the problems addressed by Elie Cartan are among the most important, most abstract and most general dealing with Mathematics; group theory is, so to speak, the whole Mathematics, stripped of its material and reduced to pure form. This extreme level of abstraction has probably made my presentation a little dry; to assess each of the results, I would have had virtually render him the material which he had been stripped; but this refund can be made in a thousand different ways; and this is the only form that can be found as well as a host of various Garments, which is the common link between mathematical theories that are often surprised to find so near”.

    From Mechanics to Geometry
    The last elaboration of geometric structure on information is emerging at the inter-relations between “Geometric Mechanics” and ”Information Theory” that will be largely debated at GSI15 conference with invited speakers as C. M. Marle, T. Ratiu and M. Arnaudon. Elie Cartan, the master of Geometry during the last century, said ”distinguished service that has rendered and will make even the absolute differential calculus of Ricci and Levi-Civita should not prevent us to avoid too exclusively formal calculations, where debauchery indices often mask a very simple geometric fact. It is this reality that I have sought to put in evidence everywhere.”.
    For the anecdote, Elie Cartan, was the son of Joseph Cartan who was the village blacksmith, and Elie recalled that his childhood had passed under ”blows of the anvil, which started every morning from dawn”. One can imagine that the hammer blows given by Joseph on the anvil, giving shape and CURVATURE to the metal, insidiously influencing Elie’s mind with germinal intuition of fundamental geometric concepts. Alliance of Geometry and Mechanics is beautifully illustrated by this image of Forge, in this painting of Velasquez about Vulcan God (see Figure 1). This concordance of meaning is also confirmed by etymology of word “Forge”, that comes from late XIV century, “a smithy,” from Old French forge “forge, smithy” (XII century), earlier faverge, from Latin fabrica “workshop, smith’s shop”, from faber (genitive fabri) “workman in hard materials, smith”.
    As Henri Bergson said in book “The Creative Evolution” in 1907: “As regards human intelligence, there is not enough noticed that mechanical invention was first its essential approach ... we should say perhaps not Homo sapiens, but Homo faber. In short, intelligence, considered in what seems to be its original feature, is the faculty of manufacturing artificial objects, especially tools to make tools, and of indefinitely varying the manufacture.”

    Geometric Science of Information: a new Grammar of Sciences
    Henri Poincaré said that “Mathematics is the art of giving the same name to different things” (“La mathématique est l’art de donner le même nom `a des choses différentes.” in “Science et méthode”, 1908). By paraphrasing Henri Poincaré, we could claim that “Geometric Science of Information” is the art of giving the same name to different sciences. The rules and the Structures developed in GSI15 conference is a kind of new Grammar for Sciences.
    schema geometric science info.jpg

  • 3rd conference on Geometric Science of Information - 1st October- 31st December 2017


    Invited & Keynote Speakers

    Guest Honorary speaker

    Jean-Michel Bismut (professeur à l’Université Paris-Sud (Orsay), member of Académie des Sciences)
    Jean-Michel Bismut was born in 1948 in Lisbon (Portugal). He studied at Ecole Polytechnique in 1967-1969, and he received his Doctorat d’Etat from Université Paris VI in 1973. He became a professor of Mathematics in Orsay in 1981. He was a plenary speaker at ICM-Berlin 1998, and a vice-president of International Mathematical Union from 2002 to 2006. His research has been devoted to stochastic control, to the Malliavin calculus, to index theory, and its connections with spectral theory and number theory.
    The hypoelliptic Laplacian
    If X is a Riemannian manifold, the hypoelliptic Laplacian is a family of hypoelliptic operators acting on X , the total space of the tangent bundle of X , that interpolates between the ordinary Laplacian and the geodesioc flow. The probabilistic counterpart is an interpolation between Brownian motion and geodesics.
    In the talk, I will explain the construction of the hypoelliptic Laplacian, and describe some of its properties.
    J.-M. Bismut. The hypoelliptic Laplacian on the cotangent bundle. J. Amer. Math. Soc., 18(2):379-476 (electronic), 2005.
    J.-M. Bismut and G. Lebeau. The hypoelliptic Laplacian and Ray-Singer metrics, volume 167 of Annals of Mathematics Studies. Princeton University Press, Princeton, NJ, 2008.
    J.-M. Bismut. Loop spaces and the hypoelliptic Laplacian. Comm. Pure Appl. Math., 61(4):559-593, 2008.
    J.-M. Bismut. Hypoelliptic Laplacian and orbital integrals, volume 177 of Annals of Mathematics Studies. Princeton University Press, Princeton, NJ, 2011.

    Invited Honorary speaker

    Daniel Bennequin (Université Paris 7 - Institut Mathématique de Jussieu)
    Born 3 January 1952. Graduate from Ecole Normale Supérieure. PHD in 1982 with Alain Chenciner at Paris VII. Then Professor at Strasbourg University. Today Professor at Paris-Diderot University, and member of the IMJ. During the 1980’s he was initiator of contact topology with Y.Eliashberg. During the 1990’s, he worked on integrable systems and geometry of Mathematical Physics. Since 2000 he has been working in Neurosciences (mainly with A.Berthoz, C-d-F, and T.Flash, Weizmann Institute); he made contributions to the study of human movements duration, vestibular informatin flow and gaze functions during locomotion. His most recent publications are on information topology (with P.Baudot), psychic pain (with M.Bompard-Porte) and labyrinths (with R.David et al.).
    Geometry and Vestibular Information
    Every complex living entities, as plants, insects or vertebrates, possess visuo-vestibular systems which sense their own motion in space and are crucial for controling volontary movements and for understanding space. We will show how the Galilée group guides the visuo-vestibular information flows. Differential Geometry permits to understand the particular forms of the end vestibular organs, that are situated in the inner ear of mammals and birds, from a principle of energy minimization and information maximization. These forms correspond to the surfaces of divisors of real (resp. imaginary) twisted curves, for the epithelia which sense linear accelerations (resp. rotations) of the head. The Hodge-DeRham theory, applied to the labyrinths volume of vertebrates, permits to explain how a complex fluid movement is transformed in six solutions of ordinary second order differential equations, for registering the head rotations in space. Combined with an original and delicate method of analysis of the membranous tissues, invented by Romain David, this allows for the first time, to describe the precise relation between the structure and the function of the labyrinth.
    R.David, A.Stoessel, A.Berthoz, F.Spoor, D.Bennequin, “Assessing morphology and function of the semicircular duct system: introducing new in situ visualization and software toolbox ”, Scientific Reports, 2016.
    P.Marianelli, A.Berthoz, D.Bennequin, “Crista egregia: a geometrical model of the crista ampullaris, a sensory surface that detects head rotations”, Biological Cybernetics, 2015.
    M.Dimiccoli, B.Girad, A.Berthoz, D.Bennequin, “Striola Magica. A functional explanation of the otolith geometry”, J Comput Neurosciences, 2013.
    D.Bennequin, A.Berthoz, “Non-linear Galilean receptive fields”, IEEE Med Biol Soc, Boston, 2011

    Keynote speakers

    Alain Trouvé (ENS Paris-Saclay, CMLA Department)
    Alain Trouvé, bachelor’s degree from Ecole Normale Supérieure Ulm, a doctor of the University of Orsay, began his career as “agrégé préparateur” at the ENS Ulm before becoming a professor at the University of Paris13 (1996) and then at ENS Cachan (2003). Alain Trouvé is currently Professor at the Center of Mathematics and Their Application (CMLA) at ENS Paris-Saclay. He did his Ph.D. in Stochastic Optimization and Bayesian Image Analysis under the supervision of Robert Azencott. His main research interests are computational vision and shape analysis with a particular emphasis on the use of Riemannian geometry and infinite dimensional group actions driven by applications in computational anatomy and medical imaging.
    Hamiltonian modeling for shape evolution and Statistical modeling of shapes variability
    In his book "Growth and Forms", first published in 1917, d’Arcy Thompson, a Scottish naturalist and mathematician, develops his theory of transformations, whose central idea is the morphological comparison of anatomies through groups of transformations of Space that act on it. This idea, a century later, remains at the heart of contemporary geometric approaches of quantitative comparison of forms but in a very different mathematical and technological context. In this talk, we present the ideas and techniques that underlie the "diffeomorphometric" approach developed in the context of computational anatomy, its links with infinite dimensional Riemannian geometry, the theory of control And Hamiltonian systems, but also the dimension reduction tools that underlie the algorithms used in the analysis of sub-varieties and make them effective. We will also present new prospects for extension on the geometric-functional objects that combine geometric and functional information and pose new and numerous challenges.
    Devilliers, L., Allassonnière, S., Trouvé, A., & Pennec, X. (2017). Inconsistency of Template Estimation by Minimizing of the Variance/Pre-Variance in the Quotient Space. Entropy, 19(6), 288.
    Lee, S., Charon, N., Charlier, B., Popuri, K., Lebed, E., Sarunic, M. V., ... & Beg, M. F. (2017). Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework. Medical image analysis, 35, 570-581.
    D. Tward, M. Miller, A. Trouvé and L. Younes, "Parametric Surface Diffeomorphometry for Low Dimensional Embeddings of Dense Segmentations and Imagery," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1195-1208, June 1 2017.
    Charlier, B., Charon, N., & Trouvé, A. (2017). The fshape framework for the variability analysis of functional shapes. Foundations of Computational Mathematics, 17(2), 287-357.
    M. Miller, L. Younes and A. Trouve. "Hamiltonian Systems in Computational Anatomy : 100 Years since D'Arcy Thompson". Annual Review of Biomedical Engineering 17 , 2015
    S. Arguillere, E. Trélat, A. Trouvé, L. Younes. "Shape deformation analysis from the optimal control point of view". Journal de Mathématiques Pures et Appliquées 104 (1): 139-178, 2015 Mark Girolami (Imperial College London - Department of Mathematics)
    Mark Girolami holds a Chair in Statistics in the Department of Mathematics of Imperial College London. He is an EPSRC Established Career Research Fellow (2012 - 2017) and previously an EPSRC Advanced Research Fellow (2007 - 2012). He is the Director of the Alan Turing Institute-Lloyds Register Foundation Programme on Data Centric Engineering and in 2011 was elected to the Fellowship of the Royal Society of Edinburgh when he was also awarded a Royal Society Wolfson Research Merit Award. He was one of the founding Executive Directors of the Alan Turing Institute for Data Science from 2015 to 2016. He has been nominated by the IMS to deliver a Medallion Lecture at JSM 2017 and has been invited to give a Forum Lecture at the European Meeting of Statisticians 2017. His paper on Riemann manifold Langevin and Hamiltonian Monte Carlo Methods was publicly read before the Royal Statistical Society and received the largest number of contributed discussions for any paper in the entire history of the society, discussants included Sir D.R. Cox and C.R. Rao.
    Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
    The talk considers Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis–Hastings or indeed Hamiltonian Monte Carlo and Metropolis adjusted Langevin algorithms. This allows for highly efficient sampling even in very high dimensions where different scalings may be required for the transient and stationary phases of the Markov chain. The methodology proposed exploits the Riemann geometry of the parameter space of statistical models and thus automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density. The performance of these Riemann manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, log-Gaussian Cox point processes, stochastic volatility models and Bayesian estimation of dynamic systems described by non-linear differential equations. Substantial improvements in the time-normalized effective sample size are reported when compared with alternative sampling approaches.
    Mike Betancourt, Simon Byrne, Sam Livingstone, and Mark Girolami (2016) "The Geometric Foundations of Hamiltonian Monte Carlo" to appear Bernoulli
    Oates, C., Girolami, M. and Chopin, N. Control Functionals for Monte Carlo Integration. To appear Journal of Royal Statistical Society - Series B, 2017.
    T.House, A.Ford, S.Lan, S. Bilson, E. Buckingham-Jeffery, M.A.Girolami. (August 2016) Bayesian Uncertainty Quantification for Transmissability of Influenza, Norovirus, and Ebola using Information Geometry. Journal of the Royal Society Interface, DOI: 10.1098/rsif.2016.0279
    Shiwei Lan, Tan Bui-Thanh, Mike Christie, Mark Girolami (2016). Emulation of Higher-Order Tensors in Manifold Monte Carlo Methods for Bayesian Inverse Problems, Journal of Computational Physics Vol. 308, 81 - 101.
    Bui, T. and Girolami, M. "Solving Large-Scale PDE-constrained Bayesian Inverse problems with Riemann Manifold Hamiltonian Monte Carlo", Inverse Problems, 30, 114014, doi:10.1088/0266-5611/30/11/114014.
    T Xifara, C Sherlock, S Livingstone, S Byrne, M Girolami. Langevin diffusions and the Metropolis-adjusted Langevin algorithm. Statistics & Probability Letters 91, 14-19, 2014.
    S Byrne, M Girolami. Geodesic Monte Carlo on Embedded Manifolds. Scandanavian Journal of Statistics, (with discussion) 40, 825 – 845, 2013.
    Girolami, M., Calderhead, B., Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods (with discussion), Journal of the Royal Statistical Society – Series B, 73(2), 123 - 214, 2011. Barbara Tumpach (Lille University/ Painlevé Laboratory)
    Alice Barbara Tumpach is an Associate Professor in Mathematics (University Lille 1, France) and member of the Laboratoire Painlevé (Lille 1/CNRS UMR 8524), since 2007. She received a Ph.D degree in Mathematics in 2005 at the Ecole Polytechnique, Palaiseau, France. She spent two years at the Ecole Polytechnique Fédérale de Lausanne as a Post-Doc, and two years at the Pauli Institut in Vienna, Austria, as an invited researcher. Her research interests lie in the area of infinite-dimensional Geometry, Lie Groups and Functional Analysis. She gives Master courses on Lie groups and organizes conferences on infinite-dimensional geometry for the Federation of Mathematical Research of Nord-Pas-Calais, France. She also acts in videos for Exo7, available on youtube, where she explains basic notions of Linear Algebra.
    Riemannian metrics on shape spaces of curves and surfaces
    The aim of the talk is to give an overview of geometric tools used in Shape Analysis. We will see that we can interpret the Shape space of (unparameterized) curves (or surfaces) either as a quotient space or as a section of the Preshape space of parameterized curves (or surfaces). Starting from a diffeomorphism-invariant Riemannian metric on Preshape space, these two different interpretations lead to different Riemannian metrics on Shape space. Another possibility is to start with a degenerate Riemannian metric on Preshape space, with degeneracy along the orbits of the diffeomorphism group. This leads to a framework where the length of a path of curves (or surfaces) does not depend on the parameterizations of the curves (or surfaces) along the path. Of course the choice of the metrics has to be motivated either from the applications or from their mathematical behaviour. We will compare some natural metrics used in the litterature.
    A.B.Tumpach and S. Preston, Quotient elastic metrics on the manifold of arc-length parameterized plane curves, to appear in Journal of Geometric Mechanics.
    A.B.Tumpach, Gauge invariance of degenerate Riemannian metrics, Notices of AMS, April 2016.
    A.B.Tumpach, H. Drira, M. Daoudi, A. Srivastava, Gauge invariant Framework for shape analysis of surfaces, IEEE TPAMI, vol 37, 2015.
    A.B.Tumpach, Infinite-dimensional hyperkähler manifolds associated with Hermitian-symmetric affine coadjoint orbits, Annales de l'Institut Fourier, Tome 59, 2009.
    A.B.Tumpach, Classification of infinite-dimensional Hermitian-symmetric affine coadjoint orbits, Forum Mathematicum 21:3, 2009.
    D. Beltita, T. Ratiu, A.B. Tumpach, The restricted Grassmannian, Banach Lie-Poisson spaces, and coadjoint orbits, Journal of Functional Analysis 247, 2007.
    A.B.Tumpach, Hyperkähler structures and infinite-dimensional Grassmannians, Journal of Functional Analysis 243, 2007.

  • Topological and Geometrical Structure of Information - CIRM conference - August 27th- September 1st 2017


    Bandeau TGSI2017.png
    The conferences emphasize on an active participation of young researchers to discuss emerging topics of collaborative research. They are organised in half day and day sessions covering one central topic. Introductory courses open to students are proposed at the beginning of the sessions, then completed by more specialized one-hour presentation, and a session of working-discussion that tackle open questions and future development lines.

    Poster Sessions: poster sessions are every afternoon after the talks and after diner in the lounge room. Introductory presentation: Paul Bourgine : Complex Systems Digital Campus (CS-DC) Session 1: Information-theoretic geometry of metric measure spaces (particular and general).
    Organisers: Michel Ledoux (Institut de Mathématiques de Toulouse, France), Mokshay Madiman (University of Delaware, USA)
    Mini-course: Mokshay Madiman (University of Delaware, USA)
    Speakers: Thomas Courtade (University of California, Berkeley, USA), Nathael Gozlan (Université Paris-Est, France), Oliver Johnson (University of Bristol, UK), Jan Maas (IST, Austria).
    Abstract: This session will explore the geometry of particular instances as well as general classes of metric measure spaces as captured using the notion of entropy.

    Session 2: Information and topology
    Organisers: Pierre Baudot (INSERM, Fance), Daniel Bennequin (Université Paris Diderot, France), Michel Boyom (Université du Languedoc-Montpellier II, France), Herbert Gangl (Durham University, UK), Matilde Marcolli (Caltech, USA), John Terilla (Queens College, USA).
    Speakers: Pierre Baudot (INSERM, Fance), Daniel Bennequin (Université Paris Diderot, France), Michel Boyom (Université du Languedoc-Montpellier II, France), Philippe Elbaz-Vincent (Institut Fourier, France), Tom Leinster (University of Edinburgh, UK), Matilde Marcolli (Caltech, USA), John Terilla (Queens College, USA)
    Abstract: Arising from polylogarithmic functional equation, tropical semirings and probability theory studies, this session will adress the progresses acheived in caracterising the topology associated to information and probability theory, notably expressing some features of motive and operad.

    Session 3: Classical/Stochastic Geometric Mechanics and Lie Group Thermodynamics /Statistical Physics
    Organisers: Frédéric Barbaresco (Thales, France), Joël Bensoam (IRCAM, France).
    Speakers: Frédéric Barbaresco (Thales, France), Joël Bensoam (IRCAM, France), François Gay-Balmaz (LMD-ENS, France), Frédéric Hélein (Université Paris-Diderot, France), Bernhard Maschke (Claude Bernard University, France).
    Abstract: This session will address methods of variational calculus combined with stochastic analysis, Multi-Symplectic Geometry and Lie Group theories to study foundations of Stochastic Geometric Mechanics and Lie Group Thermodynamics.

    Session 4: Geometry of quantum states and quantum correlations
    Organisers: Dominique Spehner (Institut Fourier, France)
    Speakers: Madalin Guta (University of Nottingham, UK), Dominique Spehner (Institut Fourier, France), Karol Zyczkowski (Jagiellonian University, Poland) (...TBA).
    Abstract: The aim of this session is to explore the different Riemannian geometries that can be used to describe and quantify quantum correlations in composite quantum systems, together with their operational interpretations in quantum information theory.

    Session 5: Quantum states of geometry and geometry of quantum states.
    Organisers: Carlo Rovelli (Centre de Physique Theorique de Luminy, France)
    Speakers: Livine Etera (ENS Lyon, France), Antonino Marcianò (Fudan University, China), Carlo Rovelli (Centre de Physique Theorique de Luminy, France).
    Abstract: The objective of the session is to make the point on the way geometry and quantum correlations are related in quantum gravity. The focus would be on the recent developments in the possibility of describing quantum states of the geometry with long distance correlations. Session 6: Geometric Statistics on Manifolds and Shape Spaces.
    Organisers: Stéphanie Allasonnière (Paris V University, France), Xavier Pennec (INRIA sophia, France)
    Mini-course: Xavier Pennec (INRIA sophia, France), Alain Trouvé (ENS Cachan, France)
    Speakers: Marc Arnaudon (IMB, France), Aasa Feragen (DIKU, Denmark), Stanley Durrleman (ARAMIS lab, France), Ian Dryden (University of Nottingham, UK), Alice Le Brigant (IMB, Université de Bordeaux, France)
    Abstract: This session presents recent progresses in geometric statistics. In many applications domains such as computational anatomy and phylogenetics, computer vision, structural biology, one models data as elements of a manifold which is quotiented by a proper and isometric Lie group action (a shape space). Session 7: Geometry of Information for Neural Networks, Machine Learning, and Artificial Intelligence.
    Organisers: Nihat Ay (MPI-MIS, Germany), František Matúš (Institute of Information Theory and Automation, Czech Republic)
    Speakers: Nihat Ay (MPI-MIS, Germany), Tobias Fritz (Perimeter Institute, Canada), Luigi Malago (RIST, Romania), František Matúš (Institute of Information Theory and Automation, Czech Republic), Guido Montúfar (MPI-MIS, Germany), Johannes Rauh (Leibniz Universität, Germany), Milan Studený (Institute of Information Theory and Automation, Czech Republic).
    Abstract: This session will review the role in network analysis within the fields of artificial intelligence and machine learning, of geometric objects defined in terms of information equalities as well as information inequalities.

    Program Schedule:

    Sponsor TGSI2017.png

  • Geo-Sci-Info

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    Introduction and presentation of the conferences by Frederic Barbaresco. VIDEO

    Presentation of Geometric Sciences of Information and GSI 2021 by Frederic Barbaresco. VIDEO

    LECTURES (90 min)

    1. Langevin Dynamics

    1.1 Langevin Dynamics: old and news : Eric Moulines . Part 1 : introduction to Markov chain Monte Carlo Methods VIDEO, Part 2 VIDEO

    2. Computational Information Geometry:

    2.1. Information Manifold modeled with Orlicz Spaces : Giovanni Pistone . VIDEO

    2.2. Recent contributions to Distances and Information Geometry: a computational viewpoint : Frank Nielsen . VIDEO - SLIDES

    3. Non-Equilibrium Thermodynamic Geometry

    3.1. A variational perspective of closed and open systems: François Gay-Balmaz 3.2. Geometry of Non-Equilibrium Thermodynamics: a homogeneous Symplectic approach : Arjan Van Der Schaft . VIDEO- SLIDES

    4. Geometric Mechanics

    4.1. Galilean Mechanics and Thermodynamics of continua : Géry de Saxcé. VIDEO - SLIDES

    4.2. Souriau-Casimir Lie Groups Thermodynamics and Machine Learning : Frederic Barbaresco. VIDEO - SLIDES

    5. "Structure des Systèmes Dynamiques" (SSD) Jean-Marie Souriau’s book 50th Birthday Wikipedia page

    5.1. Souriau Familly and "structure of motion": Jean-Marie Souriau, Michel Souriau, Paul Souriau and Etienne Souriau : Frederic Barbaresco . VIDEO - SLIDES

    5.2. SSD Jean-Marie Souriau’s book 50th birthday: Géry de Saxcé SLIDES

    KEYNOTES (60 min)

    Learning Physics from Data : Francisco Chinesta . VIDEO VIDEO - SLIDES

    Information Geometry and Integrable Systems : Jean-Pierre Françoise. VIDEO VIDEO - SLIDES

    Learning with Few Labeled Data : Pratik Chaudhari . VIDEO - SLIDES

    Information Geometry and Quantum Fields : Kevin Grosvenor SLIDES

    Port Thermodynamic Systems Control : Bernhard Maschke . VIDEO - SLIDES

    Dirac Structures in Nonequilibrium Thermodynamics : Hiroaki Yoshimura . VIDEO - SLIDES

    Thermodynamic efficiency implies predictive inference : Susanne Still . VIDEO - SLIDES

    Computational dynamics of reduced coupled multibody-fluid system in Lie group setting : Zdravko Terze . VIDEO - SLIDES

    Exponential Family by Representation Theory : Koichi Tojo . VIDEO - SLIDES

    Deep Learning as Optimal Control Problems and Structure Preserving Deep Learning : Elena Celledoni . VIDEO - SLIDES

    Contact geometry and thermodynamical systems : Manuel de León. VIDEO - SLIDES

    Diffeological Fisher Metric : Hông Vân Lê. VIDEO - SLIDES

    Mechanics of the probability simplex : Luigi Malagò. VIDEO - SLIDES

    Covariant Momentum Map Thermodynamics : Goffredo Chirco. VIDEO - SLIDES

    Sampling and statistical physics via symmetry : Steve Huntsman. VIDEO - SLIDES

    Geometry of Measure-preserving Flows and Hamiltonian Monte Carlo : Alessandro Barp. VIDEO - SLIDES

    Schroedinger's problem, Hamilton-Jacobi-Bellman equations and regularized Mass Transportation : Jean-Claude Zambrini. VIDEO - SLIDES


    PDF of posters:

    Viscoelastic flows of Maxwell fluids with conservation laws - Sébastien Boyaval - POSTER Bayesian Inference on Local Distributions of Functions and Multi-dimensional Curves with Spherical HMC Sampling - Anis Fradi and Chafik Samir - POSTER Material modeling via Thermodynamics-based Artificial Neural Networks - Filippo Masi Ioannis Stefanou, Paolo Vannucci, Victor Maffi-Berthier - POSTER LEARNING THE LOW-DIMENSIONAL GEOMETRY OF THE WIRELESS CHANNEL - Paul Ferrand, Alexis Decurninge, Luis Garcia Ordóñez and Maxime Guillaud - POSTER A Hyperbolic approach for learning communities on graphs - Hatem Hajri, Thomas Gerald and Hadi Zaatiti - POSTER UNSUPERVISED OBJECT DETECTION FOR TRAFFIC SCENE ANALYSIS - Bruno Sauvalle (superviseur: ARNAUD DE LA FORTELLE) - POSTER Hard Shape-Constrained Kernel Regression - Pierre-Cyril Aubin-Frankowski and Zoltán Szabó - POSTER CONSTRAINT-BASED REGULARIZATION OF NEURAL NETWORKS - Benedict Leimkuhler, Timothée Pouchon, Tiffany Vlaar, Amos Storkey - POSTER CONNECTING STOCHASTIC OPTIMIZATION WITH SCHRÖDINGER EVOLUTION WITH RESPECT TO NON HERMITIAN HAMILTONIANS - C. Couto, J. Mourão, J.P. Nunes and P. Ribeiro - POSTER Geomstats: A Python Package for Geometry in Machine Learning and Information Geometry - Nina Miolane, Nicolas Guigui1, Alice Le Brigant, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Yann Cabanes, Thomas Gerald, Paul Chauchat, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec - POSTER Fast High-order Tensor Learning Based on Grassmann Manifold - O.KARMOUDA, R.BOYER and J.BOULANGER - POSTER A Geometric Interpretation of Stochastic Gradient Descent in Deep Learning and Bolzmann Machines - Rita Fioresi and Pratik Chaudhari - POSTER Lagrangian and Hamiltonian Dynamics on the Simplex - Goffredo Chirco, Luigi Malago, Giovanni Pistone - POSTER Calibrating Bayesian Neural Networks with Alpha-divergences and Normalizing Flows - Hector J. Hortua, Luigi Malago and Riccardo Volpi - POSTER


  • Geo-Sci-Info

    Capture du 2018-10-06 09-13-06.png



    A seminar on Topological and Geometrical Structures of Information has been organized at CIRM in 2017, to gather engineers, applied and pure mathematicians interested in the geometry of information. This year FGSI’19 conference will be focused on the foundations of geometric structures of information. It is dedicated to the triumvirat Cartan - Koszul - Souriau and their influence on the field.

    The conference will take place in Montpellier from Monday 4th February 2019 at 9am until Wednesday 6th February at 1pm.

    Capture du 2018-10-06 09-18-09.png


    Anton ALEKSEEV (Geneva Univ.) Dmitri ALEKSEEVSKY (Moscow IITP) John BAEZ (Riverside UC) Michel BRION (Grenoble Univ.) Misha GROMOV* (Paris IHES) Patrick IGLESIAS-ZEMMOUR (Marseille Univ.) Yann OLLIVIER (Paris Facebook) Vasily PESTUN (Paris IHES) Aissa WADE (Penn State Univ.)
    *to be confirmed

    Panel sessions :




    Capture du 2018-10-06 09-24-34.png

  • 5th conference on Geometric Science of Information in PARIS, Sorbonne University 21 July 2021 - 23 July 2021


    Informatics Institute, University of Amsterdam and Qualcomm Technologies
    ELLIS Board Member (European Laboratory for Learning and Intelligent Systems:

    Title: Exploring Quantum Statistics for Machine Learning

    Abstract: Quantum mechanics represents a rather bizarre theory of statistics that is very different from the ordinary classical statistics that we are used to. In this talk I will explore if there are ways that we can leverage this theory in developing new machine learning tools: can we design better neural networks by thinking about entangled variables? Can we come up with better samplers by viewing them as observations in a quantum system? Can we generalize probability distributions? We hope to develop better algorithms that can be simulated efficiently on classical computers, but we will naturally also consider the possibility of much faster implementations on future quantum computers. Finally, I hope to discuss the role of symmetries in quantum theories.

    Roberto Bondesan, Max Welling, Quantum Deformed Neural Networks, arXiv:2010.11189v1 [quant-ph], 21st October 2020 ;

    Jean PETITOT
    Directeur d'Études, Centre d'Analyse et de Mathématiques, Sociales, École des Hautes Études, Paris.
    Born in 1944, Jean Petitot is an applied mathematician interested in dynamical modeling in neurocognitive sciences. He is the former director of the CREA (Applied Epistemology Research Center) at the Ecole Polytechnique.

    Philisopher of science

    Title : The primary visual cortex as a Cartan engine

    Abstract: Cortical visual neurons detect very local geometric cues as retinal positions, local contrasts, local orientations of boundaries, etc. One of the main theoretical problem of low level vision is to understand how these local cues can be integrated so as to generate the global geometry of the images perceived, with all the well-known phenomena studied since Gestalt theory. It is an empirical evidence that the visual brain is able to perform a lot of routines belonging to differential geometry. But how such routines can be neurally implemented ? Neurons are « point-like » processors and it seems impossible to do differential geometry with them. Since the 1990s, methods of "in vivo optical imaging based on activity-dependent intrinsic signals" have made possible to visualize the extremely special connectivity of the primary visual areas, their “functional architectures.” What we called « Neurogeometry » is based on the discovery that these functional architectures implement structures such as the contact structure and the sub-Riemannian geometry of jet spaces of plane curves. For reasons of principle, it is the geometrical reformulation of differential calculus from Pfaff to Lie, Darboux, Frobenius, Cartan and Goursat which turns out to be suitable for neurogeometry.


    Agrachev, A., Barilari, D., Boscain, U., A Comprehensive Introduction to Sub-Riemannian Geometry, Cambridge University Press, 2020. Citti, G., Sarti, A., A cortical based model of perceptual completion in the roto-translation space, Journal of Mathematical Imaging and Vision, 24, 3 (2006) 307-326. Petitot, J., Neurogéométrie de la vision. Modèles mathématiques et physiques des architectures fonctionnelles, Les Éditions de l'École Polytechnique, Distribution Ellipses, Paris, 2008. Petitot, J., “Landmarks for neurogeometry”, Neuromathematics of Vision, (G. Citti, A. Sarti eds), Springer, Berlin, Heidelberg, 1-85, Petitot,J., Elements of Neurogeometry. Functional Architectures of Vision, Lecture Notes in Morphogenesis, Springer, 2017. Prandi, D., Gauthier, J.-P., A Semidiscrete Version of the Petitot Model as a Plausible Model for Anthropomorphic Image Reconstruction and Pattern Recognition,, 2017.

    Yvette Kosmann-Schwarzbach

    Professeur des universités honoraire ; former student of the Ecole normale supérieure Sèvres, 1960-1964; aggregation of mathematics, 1963; CNRS research associate, 1964-1969; doctorate in science, Lie derivatives of spinors, University of Paris, 1970 under supervision of André Lichnerowicz; lecturer then professor at the University of Lille (1970-1976 and 1982-1993), at Brooklyn College, New York (1979-1982), at the École polytechnique (1993-2006)

    Title: Structures of Poisson Geometry: old and new

    Abstract: How did the brackets that Siméon-Denis Poisson introduce in 1809 evolve into the Poisson geometry of the 1970's? What are Poisson groups and, more generally, Poisson groupoids? In what sense does Dirac geometry generalize Poisson geometry and why is it relevant for applications? I shall sketch the definition of these structures and try to answer these questions.


    P. Libermann and C.-M. Marle, Symplectic Geometry and Analytical Mechanics, D. Reidel Publishing Company (1987). J. E. Marsden and T. S. Ratiu, Introduction to Mechanics and Symmetry, Texts in Applied Mathematics 17, second edition, Springer (1998). C. Laurent-Gengoux, A. Pichereau, and P. Vanhaecke, Poisson Structures, Grundlehren der mathematischen Wissenschaften 347, Springer (2013). Y. Kosmann-Schwarzbach, Multiplicativity from Lie groups to generalized geometry, in Geometry of Jets and Fields (K. Grabowska et al., eds), Banach Center Publications 110, 2016. Special volume of LMP on Poisson Geometry, guest editors, Anton Alekseev, Alberto Cattaneo, Y. Kosmann-Schwarzbach, and Tudor Ratiu, Letters in Mathematical Physics 90, 2009. Y. Kosmann-Schwarzbach (éd.), Siméon-Denis Poisson : les Mathématiques au service de la science, Editions de l'Ecole Polytechnique (2013). Y. Kosmann-Schwarzbach, The Noether Theorems: Invariance and Conservation Laws in the Twentieth Century, translated by B. E. Schwarzbach, Sources and Studies in the History of Mathematics and Physical Sciences, Springer (2011).

    Michel Broniatowski


    Sorbonne Université, Paris

    Title: Some insights on statistical divergences and choice of models

    Abstract: Divergences between probability laws or more generally between measures define inferential criteria, or risk functions. Their estimation makes it possible to deal with the questions of model choice and statistical inference, in connection with the regularity of the models considered; depending on the nature of these models (parametric or semi-parametric), the nature of the criteria and their estimation methods vary. Representations of these divergences as large deviation rates for specific empirical measures allow their estimation in nonparametric or semi parametric models, by making use of information theory results (Sanov's theorem and Gibbs principles), by Monte Carlo methods. The question of the choice of divergence is wide open; an approach linking nonparametric Bayesian statistics and MAP estimators provides elements of understanding of the specificities of the various divergences in the Ali-Silvey-Csiszar-Arimoto class in relation to the specific choices of the prior distributions.


    Broniatowski, Michel ; Stummer, Wolfgang. Some universal insights on divergences for statistics, machine learning and artificial intelligence. In Geometric structures of information; Signals Commun. Technol., Springer, Cham, pp. 149.211, 2019 Broniatowski, Michel. Minimum divergence estimators, Maximum Likelihood and the generalized bootstrap, to appear in "Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems" Entropy, 2020 Csiszár, Imre ; Gassiat, Elisabeth. MEM pixel correlated solutions for generalized moment and interpolation problems. IEEE Trans. Inform. Theory 45, no. 7, 2253–2270, 1999 Liese, Friedrich; Vajda, Igor. On divergences and informations in statistics and information theory. IEEE Trans. Inform. Theory 52, no. 10, 4394–4412, 2006

    Maurice de Gosson

    Professor, Senior Researcher at the University of Vienna
    Faculty of Mathematics, NuHAG group

    Title: Gaussian states from a symplectic geometry point of view

    Abstract: Gaussian states play an ubiquitous role in quantum information theory and in quantum optics because they are easy to manufacture in the laboratory, and have in addition important extremality properties. Of particular interest are their separability properties. Even if major advances have been made in their study in recent years, the topic is still largely open. In this talk we will discuss separability questions for Gaussian states from a rigorous point of view using symplectic geometry, and present some new results and properties.


    M. de Gosson, On the Disentanglement of Gaussian Quantum States by Symplectic Rotations. C.R. Acad. Sci. Paris Volume 358, issue 4, 459-462 (2020) M. de Gosson, On Density Operators with Gaussian Weyl symbols, In Advances in Microlocal and Time-Frequency Analysis, Springer (2020) M. de Gosson, Symplectic Coarse-Grained Classical and Semiclassical Evolution of Subsystems: New Theoretical Aspects, J. Math. Phys. no. 9, 092102 (2020) E. Cordero, M. de Gosson, and F. Nicola, On the Positivity of Trace Class Operators, to appear in Advances in Theoretical and Mathematical Physics 23(8), 2061–2091 (2019) E. Cordero, M. de Gosson, and F. Nicola, A characterization of modulation spaces by symplectic rotations, to appear in J. Funct. Anal. 278(11), 108474 (2020)

    Giuseppe LONGO
    Centre Cavaillès, CNRS & Ens Paris and School of Medicine, Tufts University, Boston

    Title: Use and abuse of "digital information" in life sciences, is Geometry of Information a way out?

    Abstract: Since WWII, the war of coding, and the understanding of the structure of the DNA (1953), the latter has been considered as the digital encoding of the Aristotelian Homunculus. Till now DNA is viewed as the "information carrier" of ontogenesis, the main or unique player and pilot of phylogenesis. This heavily affected our understanding of life and reinforced a mechanistic view of organisms and ecosystems, a component of our disruptive attitude towards ecosystemic dynamics. A different insight into DNA as a major constraint to morphogenetic processes brings in a possible "geometry of information" for biology, yet to be invented. One of the challenges is in the need to move from a classical analysis of morphogenesis, in physical terms, to a "heterogenesis" more proper to the historicity of biology.


    Arezoo Islami, Giuseppe Longo. Marriages of Mathematics and Physics: a challenge for Biology, Invited Paper, in The Necessary Western Conjunction to the Eastern Philosophy of Exploring the Nature of Mind and Life (K. Matsuno et al., eds), Special Issue of Progress in Biophysics and Molecular Biology, Vol 131, Pages 179¬192, December 2017. (DOI) (SpaceTimeIslamiLongo.pdf) Giuseppe Longo. How Future Depends on Past Histories and Rare Events in Systems of Life, Foundations of Science, (DOI), 2017 (biolog-observ-history-future.pdf) Giuseppe Longo. Information and Causality: Mathematical Reflections on Cancer Biology. In Organisms. Journal of Biological Sciences, vo. 2, n. 1, 2018. (BiologicalConseq-ofCompute.pdf) Giuseppe Longo. Information at the Threshold of Interpretation, Science as Human Construction of Sense. In Bertolaso, M., Sterpetti, F. (Eds.) A Critical Reflection on Automated Science – Will Science Remain Human? Springer, Dordrecht, 2019 (Information-Interpretation.pdf) Giuseppe Longo, Matteo Mossio. Geocentrism vs genocentrism: theories without metaphors, metaphors without theories. In Interdisciplinary Science Reviews, 45 (3), pp. 380-405, 2020. (Metaphors-geo-genocentrism.pdf)

  • Information Geometry and its Applications IV - June 13-17, 2016, Liblice, Czech Republic -

  • Big Data Mathematical and Statistical Tools for Life Science - May 14-21 2016- Amirkabir University of Teheran-IPM, Tehran, Iran





    Saturday 14 : Opening and Introductive tutorials

    10h00:11h00 Opening and officials Welcome words of:
    Workshop general chairman,
    AUT President,
    Mathematical Department Director,
    IPM Mathematical Director
    International relations of AUT (Amir Golroo) and CS (Marc Zolver)
    Welcome of Mina Aminghafari and Adel Mohammadpour 11h00:12h30 Introductory tutorial on Big Data (Ali Mohammad-Djafari)
    Hierarchical Models and Variational Bayesian Approximation for Learning and Inference for Big Data 12h30 :14h00 Lunch 14h00:17h00 Introductory tutorial on Big Data (?)

    Sunday 15 : Mathematical and Statistical tools 1

    09h00:10h30 François Orieux : Fast MCMC algorithm for large scales inverses problems
    * 10h30:11h00 Tea and coffee break 11h00:12h30 Jean-François Giovannelli : Segmentation of piecewise constant images from incomplete, distorted and noisy data 12h30 :14h00 Lunch 14h00:17h00 Frédéric Pascal:

    Monday 16 : Mathematical and Statistical tools 2

    09h00:10h30 Stéphane Robin: Exact Bayesian inference for some models with discrete parameters 10h30:11h00 Tea and coffee break 11h00:12h30 Abdolreza Sayyareh: Non-nested and misspecified model selection for big data
    12h30 :14h00 Lunch 14h00:17h00 Pierre Baudot :Topological structures of information: theory, perspectives and applications to biological data and biological models.

    Tuseday 17 : Applications in Life Science

    09h00:10h30 Medical and Biomedical imaging systems
    Vincent Vigneron : Alzheimer's disease early detection with Deep-learning. The machine learning support
    10h30:11h00 Tea and coffee break 11h00:12h30 Environnemental applications
    Dominique Laffly: 12h30 :14h00 Lunch 14h00:17h00 Gholamreza Nakhaeizadeh: Applications of Big Data Mining in the Healthcare Industry

    Wednesday 18 : Applications in Life Science

    09h00:10h30 Medical and Biomedical imaging systems
    S. Morteza Najibi: Protein Classification and Prediction Using Multiple Ramachandran Distributions
    Abolfazl Fatholahzadeh:Building Incremental Homgraphs of Big Data
    10h30:11h00 Tea and coffee break 11h00:12h30 Medical and Biological imaging systems 12h30 :14h00 Lunch 14h00:17h00 Medical and Biological imaging systems

    Thursday 19 : Scientific and Touristic visit of Isfahan

  • 12-17 sept 2016 Quiberon France - 5th summer school of theoretical Mecanic


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    Télécharger l'affiche


    La cinquième école d’été de mécanique théorique portera sur les méthodes géométriques en mécanique : introduction à la géométrie différentielle et à la géométrie Riemannienne, mécaniques Lagrangienne et Hamiltonienne, symétries et théorème de Noether, intégrateurs variationnels, point de vue géométrique sur la
    mécanique des milieux continus et sur la thermodynamique.

    École Thématique du CNRS, gratuite pour les personnels CNRS au titre de la formation permanente (nombre limité deplaces gratuites). Une subvention de l’AUM permet d’offrir également un tarif réduit à tous les académiques hors CNRS.

    Intervenants :

    Boris Kolev
    CMI, Marseille, France. Tudor Ratiu
    EPFL, Lausanne, Suisse. Gery de Saxcé
    Université des Sciences et Technologies de Lille 1, Laboratoire de Mécanique de Lille, Lille, France

    Comité d'organisation

    Patrick Ballard, Paris Aziz Hamdouni, La Rochelle Jean Lerbet, Évry Jean-Jacques Marigo, Palaiseau

    Comité Scientifique

    Patrick Ballard, Paris Anne-Sophie Bonnet-Ben Dhia, Palaiseau Michel Bornert, Marne-la-Vallée Alain Cimetière, Poitiers Gilles Francfort, Villetanneuse Aziz Hamdouni, La Rochelle Djimédo Kondo, Paris Jean Lerbet, Évry Jean-Jacques Marigo, Palaiseau Sébastien Neukirch, Paris Géry de Saxcé, Lille

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  • 26-29 July 2016 at DIMACS, Rutgers University, NJ, USA


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    DIMACS Workshop on Distance Geometry: Theory and Applications
    26-29 July 2016 at DIMACS, Rutgers University, NJ, USA



    Farid Alizadeh (Rutgers Univ.) Leo Liberti (CNRS and Ecole Polytechnique, France)

    Scientific advisory committee:

    Amir Ali Ahmadi (Princeton) Marcia Fampa (Univ. Fed. Rio de Janeiro) Bill Jackson (Queen Mary, Univ. London) Nathan Krislock (Northern Illinois Univ.) Monique Laurent (CWI, The Netherlands) Therese Malliavin (CNRS Institut Pasteur) Michel Petitjean (Univ. of Paris 7) Nicolas Rojas (Yale) Amit Singer (Princeton) Henry Wolkowicz (Univ. Waterloo) Yinyu Ye (Stanford)

    Organization: DIMACS (Tami Carpenter, Rebecca Wright)

    Distance Geometry (DG) is a field of geometry which focuses on defining and working with geometrical objects using distances between points rather than the points themselves. From classical results such as Heron's theorem, Euler's conjecture on the rigidity of polyhedra, Maxwell's forces diagrams, and the link to positive semidefinite matrices, DG has seen a veritable "engineering renaissance" in the XX century. DG is used in architecture (rigidity of structures), spatial conformation of molecules from inter-atomic distances, localization of mobile sensors in communication networks, control of unmanned underwater vehicles, control of robotic arms, solution of problems in spatial logic, and more. One of the foremost problems in DG is that of completing a partially specified matrix so that it is a Euclidean distance matrix, either in a given dimension, or in any (unspecified) dimension. Schoenberg's link means that DG is tightly linked to Semidefinite Programming (SDP), which is one of the most popular tools to solve DG problems, especially in the field of sensor networks. Because so many diverse application fields appeal to DG, its development has been somewhat fragmented, with very similar concepts being introduced within separate communities with different names. The aims of this conference are: (i) to attempt to reconcile some of this fragmentation by inviting researchers from many different disciplines to take part; (ii) to facilitate communications of technical knowledge between the different application field communities working on DG; (iii) to provide incentives for unifying the field of DG.

    The workshop will be based on a series of invited tutorials and lectures. So far, the following people have accepted to speak. They are listed in no particular order, and the list is subject to change. Bon Connelly (Cornell), Bill Jackson (QM, Univ. London), Henry Wolkowicz (Univ. Waterloo), Amit Singer (Princeton), Jon Lee (UMich), Steven Gortler (Harvard), Therese Malliavin (Institut Pasteur, Paris), Ileana Streinu (Smith College), Shin-Ichi Tanigawa (Kyoto Univ.), Abdo Alfakih (UWindsor, Canada), Carlile Lavor (Univ. Campinas), Jayme Swarczfiter (Univ. Fed. Rio de Janeiro), Amir Ali Ahmadi (Princeton), Man-Cho So (Chinese Univ. Hong Kong), Marcia Fampa (Univ. Fed. Rio de Janeiro), Tibor Jordan (Eotvos Lorand Univ.), Georgina Hall (Princeton), Frank Parmenter (MIT), Hamza Fawzi (MIT), Pablo Parrilo (MIT), Antonios Varvitsiotis (Nat. Univ. Singapore), Nathan Krislock (Northern Illinois Univ.), Onur Ozyesil (Princeton), Simon Billinge (Columbia), Douglas Goncalves (Univ. Fed. Santa Catalina, Brazil), Martin Vetterli (EPFL).

    We are organizing a poster session, for which we are calling for posters. Please write to Leo Liberti if you're interested in presenting a poster.

    A special issue of Discrete Applied Mathematics, dedicated to the topic of this workshop, will be guest edited by the co-chairs.

  • Geo-Sci-Info

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    Information Geometry for Signal Processing and Communications
    Session of the 9th IEEE Sensor Array and Multichannel Signal Processing
    10th-13th July 2016, Rio de Janeiro, Brazil



    Charles C. Cavalcante Sueli I. R. Costa

    Technical Program
    The SAM Workshop is an important IEEE Signal Processing Society event dedicated to sensor array and multichannel signal processing. The organizing committee invites the international community to contribute with state-of-the-art developments in the field. SAM 2016 will feature plenary talks by leading researchers in the field as well as poster and oral sessions with presentations by the participants.

    Welcome to Rio de Janeiro! - The workshop will be held at the Pontifical Catholic University of Rio de Janeiro, located in Gávea, in a superb area surrounded by beaches, mountains and the Tijuca National Forest, the world's largest urban forest. Rio de Janeiro is a world renowned city for its culture, beautiful landscapes, numerous tourist attractions and international cuisine. The workshop will take place during the first half of July about a month before the 2016 Summer Olympic Games when Rio will offer plenty of cultural activities and festivities, which will make SAM 2016 a memorable experience.

    Research Areas
    Authors are invited to submit contributions in the following areas:
    Adaptive beamforming
    Array processing for biomedical applications
    Array processing for communications
    Big data
    Blind source separation and channel identification
    Computational and optimization techniques
    Compressive sensing and sparsity-based signal processing
    Detection and estimation
    Direction-of-arrival estimation
    Distributed and adaptive signal processing
    Intelligent systems and knowledge-based signal processing
    Microphone and loudspeaker array applications
    MIMO radar
    Multi-antenna systems: multiuser MIMO, massive MIMO and space-time coding
    Multi-channel imaging and hyperspectral processing
    Multi-sensor processing for smart grid and energy
    Non-Gaussian, nonlinear, and non-stationary models
    Optimization techniques
    Performance evaluations with experimental data
    Radar and sonar array processing
    Sensor networks
    Source Localization, classification and tracking
    Synthetic aperture techniques
    Space-time adaptive processing
    Statistical modelling for sensor arrays
    Tensor signal processing
    Waveform diverse sensors and systems

    Submission of papers - Full-length papers with 4 pages of content and 1 extra page only for references should be electronically submitted.

    Submission of Signal Processing Letters papers - Authors of IEEE Signal Processing Letters (SPL) papers will be given the opportunity to present their work at SAM 2016, subject to space availability and approval by the Technical Program Chairs. SPL papers published between 1st June, 2015 and 31st May, 2016 are eligible for presentation at SAM 2016. Requests for presentation of SPL papers should be made by emailing the Technical Program Chairs by 31st May, 2016. Approved requests for presentation must have one author/presenter registered for SAM 2016.

  • 4-8 april 2016, Nantes, France, GdR GeoSto Annual Meeting


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    Nantes, du 4 avril au 8 avril ( campus de la faculté des Sciences de l'Université de Nantes)


    Comité d'organisation : Jean-Christophe Breton, David Coupier, Bernard Delyon, Frédéric Lavancier, Ronan Le Guével, Nathalie Krell, Nicolas Pétrélis, Anne Philippe, Paul Rochet

    En partenariat avec le GDR 3477, la conférence Géométrie Stochastique a vocation à réunir annuellement les chercheurs qui étudient d'un point de vue théorique ou appliqué des modèles spatiaux aléatoires. Pour cette 5ème édition, les thèmes étudiés seront la géométrie aléatoire, les processus ponctuels, la statistique spatiale, les champs spatiaux, les processus spatio-temporels.

    La conférence se tiendra du 6 au 8 avril et sera précédée d'une école les 4 et 5 avril. Cette école proposera deux
    mini-cours :

    Introduction to random fields and scale invariance par Hermine Biermé (Poitiers) Introduction to Spatial Point Pattern Analysis par Yongtao Guan (Miami).

    Les incriptions sont ouvertes jusqu'au 31 janvier 2016 pour l'école et jusqu'au 27 février pour la conférence, dans la limite d'environ 70 participants.

    Comité d'organisation:

    Jean-Christophe Breton (Rennes 1) David Coupier (Lille 1) Bernard Delyon (Rennes 1) Nathalie Krell (Rennes 1) Frédéric Lavancier (Nantes) Ronan Le Guével (Rennes 2) Nicolas Pétrélis (Nantes) Anne Philippe (Nantes) Paul Rochet (Nantes)

    Orateurs confirmés

    Mathieu Carriere (Inria Saclay, Ile de France) Aurélie Chapron (Université de Rouen) Jean-François Coeurjolly (Université Pierre Mendès France, Grenoble 2) Yann Demichel (Université Nanterre Paris Ouest) Anne Estrade (Université Paris Descartes, Paris 5) Edith Gabriel (Université d'Avignon et des pays de Vaucluse) Yongtao Guan (University of Miami, États-Unis) Jonas Kahn (CNRS et Université de Toulouse) Simon Le Stum (Université Lille 1) José Rafael León (Universidad Central de Venezuela) Bertrand Michel (Université Pierre et Marie Curie, Paris 6) Jesper Møller (Aalborg University, Danemark) Werner Nagel (Jena Universität, Allemagne) Giovanni Peccati (University of Luxembourg) Nicolas Privault (Nanyang Technological University, Singapour) Arnaud Rousselle (Université de Bourgogne) Kaspar Stucki (Chalmers University, Suède) Donatas Surgailis (Vilnius University, Lituanie) Joseph Yukich (Lehigh University, États-Unis)

    Ecole (lundi 4 et mardi 5 avril)

    Accueil lundi 4 avril à 10h00
    Premier cours à 10h30

    Conférence (programme indicatif)

    Mercredi 6 avril

    9h30-10h Accueil et café
    10h00-10h40 J. R. Leon
    10h40-11h20 D. Surgailis
    11h20-12h00 A. Estrade
    12h00-14h00 déjeuner
    14h00-14h40 B. Michel
    14h40-15h20 M. Carriere
    15h20-16h00 pause café
    16h00-16h40 J. Kahn
    16h40-17h20 S. Le Stum
    17h20- ... Gdr meeting

    Jeudi 7 avril

    9h00-9h40 J. Moller
    9h40-10h20 Y. Guan
    10h20-11h00 pause café
    11h00-11h40 J.-F. Coeurjolly
    11h40-12h20 E. Gabriel
    12h20-14h20 déjeuner
    14h20-15h00 G. Peccati
    15h00-15h40 N. Privault
    15h40-16h20 café
    16h20-17h00 K. Stucki
    17h00-17h40 A. Rousselle

    Vendredi 8 avril

    9h00-9h40 W. Nagel
    9h40-10h20 J. Yukich
    10h20-11h00 pause café
    11h00-11h40 Y. Demichel
    11h40-12h20 A.Chapron
    12h20-14h20 déjeuner

  • Geo-Sci-Info

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    Outils en géométrie de l'information et probabilités dans les espaces abstraits pour le traitement du signal et des images
    Date : 4 décembre 2015

    Introduction :
    Le domaine de la géométrie de l'information et des probabilités dans les espaces abstraits (variétés différentielles, espaces métriques, graphes), qui s'appuient sur des résultats de mathématiciens, physiciens et de statisticiens de renoms tels que, sans être exhaustif, Fréchet, Koszul, Souriau, Balian, Fisher, Rao, Chentsov, Amari, offre aujourd'hui un cadre mature propice à générer de nouvelles avancées pour la communauté des traiteurs du signal et de l'image au sens large. En effet, abordant les problèmes de détection, d'estimation ou de classification sous l'angle de la géométrie différentielle et de la géométrie dans les espaces métriques, la géométrie de l'information et les probabilités dans les espaces abstraits permettent d'envisager des solutions à la fois élégantes et numériquement efficaces à de nombreux problèmes génériques en traitement du signal et de l'image, classiquement traités par l'algèbre linéaire. Enfin, ces approches géométriques ont notamment l'intérêt d'exploiter des métriques invariantes et ainsi d'écarter tout arbitraire dans le choix des formes considérées ou du système de coordonnées.

    Ainsi, en géométrie de l'information, une source (signal, image, vidéo, etc.) sera vue comme un point dans un espace métrique. Un tel espace est généralement une variété dotée d'une métrique riemannienne, ou pseudo-riemannienne grâce à laquelle il est possible de définir toute une série de grandeurs intrinsèques d'intérêt pour résoudre des problèmes visant à classer, analyser ou interpréter des signaux, images ou vidéo. En probabilité dans les espaces abstraits, il s'agit de façon similaire de redéfinir la notion de mesure et de densité sur ces variétés, ainsi que les outils statistiques associés.

    L'enjeu pour nous traiteurs du signal et de l'image est donc de savoir si l'utilisation de mesures, de critères, de lois a priori intrinsèques à ces espaces permet d'obtenir de nouveaux algorithmes, ou à défaut une meilleure connaissance de ceux qui existent déjà et une plus profonde connaissance des structures de l'information traitée.

    Les présentations :

    Présentation d'ouverture de la journée
    Frédéric Barbaresco THALES AIR SYSTEMS et yannick Berthoumieu Université de Bordeaux Groupe Signal et Image Laboratoire IMS Borne de Cramer-Rao intrinsèque sur les groupes de Lie
    Silvère Bonnabel : MINES ParisTech, PSL Research University, Centre for robotics. Lois Gaussiennes dans les espaces de matrices de covariance : nouveaux outils pour l'apprentissage statistique.
    Salem Said Université de Bordeaux Groupe Signal et Image Laboratoire IMS Quelques résultats récents en géométrie différentielle et ses applications en analyse de formes 3D et la reconnaissance d'activités humaines
    Mohamed Daoudi (Professeur), CRIStAL (UMR 9189), Telecom Lille Classification multi-utilisateurs simultanée de signaux électro-encéphalographiques par géométrie Riemannienne
    Louis Korczowski, Marco Congedo, and Christian Jutten Univ. Grenoble Alpes, GIPSA-lab Interpolation riemannienne pour l'estimation de la covariance d'un canal de communication
    Alexis Decurninge, Huawei Technologies Distance entre chemins dans une variété différentielle
    Alice Le Brigant, Université de Bordeaux et THALES AIR SYSTEMS Estimation non paramétrique de densité de probabilité sur les espaces de lois Gaussiennes munies de métriques Riemanniennes
    Emmanuel Chevallier, Ecole des Mines ParisTech, Fontainebleau Optimisation au deuxième ordre sur la variété des distributions gaussiennes
    Luigi Malago, Shinshu University Estimation adaptative pour les modèles de mélange dans les familles exponentielles
    Christophe Saint-Jean, Université de La Rochelle Détection de changements sur filtres de Kalman : utilisation de la distance entre modèles multivariés gaussiens au sens de la géométrie de l'information, estimée par tirs géodésiques ou calcul de bornes.
    Marion Pilté, Ecole des Mines ParisTech et THALES AIR SYSTEMS Consensus dans les espaces métriques CAT(k) avec topologie variable
    Bellachehab Anass, Telecom SudParis Espaces de courbes: métriques et densités
    S. Puechmorel et F. Nicol, ENAC, Toulouse

  • September 22-26 2014 University of Copenhagen - Machine Learning Lab


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    Lectures overview

    Contents of Lectures by Shun-ichi Amari
    I. Introduction to Information Geometry - without knowledge on differential geometry

    Divergence function on a manifold Flat divergence and dual affine structures with Riemannian metric derived from it Two types of geodesics and orthogonality Pythagorean theorem and projection theorem Examples of dually flat manifold: Manifold of probability distributions (exponential families), positive measures and positive-definite matrices

    II. Geometrical Structure Derived from Invariance

    Invariance and information monotonicity in manifold of probability distributions f-divergence : unique invariant divergence Dual affine connections with Riemannian metric derived from divergence: Tangent space, parallel transports and duality Alpha-geometry induced from invariant geometry Geodesics, curvatures and dually flat manifold: Canonical divergence: KL- and alpha-divergence

    III. Applications of Information Geometry to Statistical Inference

    Higher-order asymptotic theory of statistical inference – estimation and hypothesis testing Neyman-Scott problem and semiparametric model em (EM) algorithm and hidden variables

    IV. Applications of Information Geometry to Machine Learning

    Belief propagation and CCCP algorithm in graphical model Support vector machine and Riemannian modification of kernels Bayesian information geometry and geometry of restricted Boltzmann machine: Towards deep learning Natural gradient learning and its dynamics: singular statistical model and manifold Clustering with divergence Sparse signal analysis Convex optimization
    Suggested reading:
    Amari, Shun-Ichi. Natural gradient works efficiently in learning. Neural Computation 10, 2 (1998): 251-276.
    Amari, Shun-ichi, and Hiroshi Nagaoka. Methods of information geometry. Vol. 191. American Mathematical Soc., 2007.

    Contents of Lectures by Nihat Ay
    I. Differential Equations:

    Vector and Covector Fields Fisher-Shahshahani Metric, Gradient Fields m- and e-Linearity of Differential Equations

    II. Applications to Evolution:

    Lotka-Volterra and Replicator Differential Equations "Fisher's Fundamental Theorem of Natural Selection" The Hypercycle Model of Eigen and Schuster

    III. Applications to Learning:

    Information Geometry of Conditional Models Amari's Natural Gradient Method Information-Geometric Design of Learning Systems

    Contents of Lectures by Nikolaus Hansen
    I. A short introduction to continuous optimization
    II. Continuous optimization using natural gradients
    III. The Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
    IV. A short introduction into Python (practice session, see also here)
    V. A practical approach to continuous optimization using (practice session)
    Suggested reading:
    Hansen, Nikolaus. The CMA Evolution Strategy: A Tutorial, 2011
    Ollivier, Yann, Ludovic Arnold, Anne Auger, and Nikolaus Hansen. Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles. arXiv:1106.3708

    Contents of Lectures by Jan Peters
    Suggested reading:
    Peters, Jan, and Stefan Schaal. Natural actor critic. Neurocomputing 71, 7-9 (2008):1180-1190

    Contents of Luigi Malagò
    Stochastic Optimization in Discrete Domains
    I. Stochastic Relaxation of Discrete Optimization Problems
    II. Information Geometry of Hierarchical Models
    III. Stochastic Natural Gradient Descent
    IV. Graphical Models and Model Selection
    V. Examples of Natural Gradient-based Algorithms in Stochastic Optimization
    For the gradient flow movie click here.
    Suggested reading:
    Amari, Shun-Ichi. Information geometry on hierarchy of probability distributions IEEE Transactions on Information Theory 47, 5 (2001):1701-1711

    Contents of Lectures by Aasa Feragen and François Lauze
    I. Aasa's lectures

    Recap of Differential Calculus Differential manifolds Tangent space Vector fields Submanifolds of R^n Riemannian metrics Invariance of Fisher information metric If time: Metric geometry view of Riemannian manifolds, their curvature and consequences thereof

    II. François's lectures

    Riemannian metrics Gradient, gradient descent, duality Distances Connections and Christoffel symbols Parallelism Levi-Civita Connections Geodesics, exponential and log maps Fréchet Means and Gradient Descent

    Suggested reading:
    Sueli I. R. Costa, Sandra A. Santos, and Joao E. Strapasson. Fisher information distance: a geometrical reading. arXiv:1106.3708

    Contents of Tutorial by Stefan Sommer
    In the tutorial on numerics for Riemannian geometry on Tuesday morning, we will discuss computational representations and numerical solutions of some differential geometry problems. The goal is to be able to implement geodesic equations numerically for simple probability distributions, to visualize the computed geodesics, to compute Riemannian logarithms, and to find mean distributions. We will follow the presentation in the paper Fisher information distance: a geometrical reading from a computational viewpoint.
    The tutorial is based on an ipython notebook that is available here. Please click here for details.


    Principles of Information Geometry have been successfully applied in all major areas of machine learning, including supervised, unsupervised, and reinforcement learning, as well as in stochastic optimization. Information Geometry comes into play when we consider parametrized probabilistic models (e.g., in the context of stochastic behavioral policies, search distributions, stochastic neural networks, ...) and their adaptation. Technically speaking, in Information Geometry the space of probability distributions that can be represented by a parametrized probabilistic model is described as a manifold, on which the Fisher information metric defines a Riemannian structure. Through the geometry of the Riemannian manifold of distributions, optimization and statistics can be done directly on the space of distributions.
    Information geometry was founded and pioneered by Shun'ichi Amari in the 1980s, with statistical learning as one of the first applications. Due to the nonlinear nature of the space of distributions, the steepest ascent direction for adapting a probability distribution parametrized by a set of real-valued parameters (e.g., the mean and the covariance of a Gaussian distribution) is not the ordinary gradient in Euclidean space, but the so called natural gradient, defined with respect to the Riemannian structure of the space of distributions. The natural gradient is natural in the sense that it renders the adaptation invariant under reparametrization and changing representations, and it is closely linked to the Kullback-Leibler divergence often used for quantifying the similarity of distributions.
    The natural gradient for adapting probabilistic models has been successfully used in all major areas of machine learning, from supervised learning of neural networks over independent component analysis to reinforcement learning. In this PhD course there will, in particular, be lectures on supervised learning, reinforcement learning and stochastic optimization. Reinforcement learning refers to machine learning algorithms that improve their behavior based on interaction with the environment, whereas stochastic optimization refers to stochastic solutions to complex optimization problems for which we do not have an analytical description. Both in stochastic optimization and reinforcement learning, (intermediate) solutions are best described by probability distributions. In the one case, we consider distributions over potential actions to be taken in a certain situation. In the other case, we consider the search distribution describing which candidate solution to probe next. Thus, both the learning as well as the optimization process are best described by an iterative update of probability distributions.

    Confirmed Speakers

    Shun'ichi Amari, RIKEN Brain Science Institute Nihat Ay, Max Planck Institute for Mathematics in the Sciences and Universität Leipzig Nikolaus Hansen, Université Paris-Sud and Inria Saclay – Île-de-France Jan Peters, Technische Universität Darmstadt and Max-Planck Institute for Intelligent Systems Luigi Malagò, Shinshu University, Nagano Aasa Feragen, University of Copenhagen Francois Lauze, University of Copenhagen Stefan Sommer, University of Copenhagen

    Scientific content
    The course will consist of 5 days of lectures and exercises. In addition, students will be expected to read a pre-defined set of scientific articles on information geometry prior to the course, and write a report on information geometry and its potential use in their own research field after the course. The course will consist of three modules:

    A crash course on Riemannian geometry and numerical tools for applications of Riemannian geometry Introduction to Information Geometry and its role in Machine Learning and Stochastic Optimization Applications of Information Geometry

    Learning goals
    After participating in this course, the participant should

    Understand basic differential geometric concepts (manifolds, Riemannian metric, geodesics, manifold statistics) to the point where they can apply differential geometric concepts in their own research; Be able to implement basic numerical tools for differential geometric computations; Have a strong knowledge of information geometry and its role in machine learning and stochastic optimization; Be able to apply information theoretic approaches to machine learning and stochastic optimization in their own research;
    Have a basic knowledge of existing applications of information geometry.


    Christian Igel, University of Copenhagen Aasa Feragen, University of Copenhagen

    Københavns Universitet, Njalsgade 128, Bygning (building) 27, Lokal (room): 27.0.17

    The lectures are at the south campus of the University of Copenhagen, very close to the Metro station Islands Brygge. Room 27.0.17 in building 27 is on the ground floor. Click here for a map. See also Google maps.

  • Geo-Sci-Info

    Transition to Exascale Computing

    Target European proposal: FETHPC-02-2017

    Topic: Transition to Exascale Computing Subtopic: Mathematics and algorithms for extreme scale HPC systems and applications working with extreme data Types of action: RIA Research and Innovation action Planned opening : date Single-stage 12 April 2017 Deadline: 26 September 2017 17:00:00


    Topic description

    Specific Challenge:

    Take advantage of the full capabilities of exascale computing, in particular through high-productivity programming environments, system software and management, exascale I/O and storage in the presence of multiple tiers of data storage, supercomputing for extreme data and emerging HPC use modes, mathematics and algorithms for extreme scale HPC systems for existing or visionary applications, including data-intensive and extreme data applications in scientific areas such as physics, chemistry, biology, life sciences, materials, climate, geosciences, etc.

    Scope: Proposals should address one or more of the following subtopics:

    a) High productivity programming environments for exascale: Proposals should have as target to simplify application software development for large- and extreme-scale systems. This can include the development of more productive programming models and environments, the easier combination of different programming models, and using increased intelligence throughout the programming environment. Key aspects include managing data transfers, data locality and memory management, including support for heterogeneous and reconfigurable systems as well as dealing with inter-application dynamic load balancing and malleability, adapting to changes in the number of processors. Unified performance tools are required supporting HPC, embedded and extreme data workloads, on diverse target systems. APIs, runtime systems and the underlying libraries should support auto-tuning for performance and energy optimisation. Automated support for debugging and anomaly detection is also included under this subtopic. To provide simplified development and to ensure the maintainability of domain-specific languages (DSLs), DSL frameworks are required which target a general-purpose stable programming model and runtime. Since large future systems will require the use of multiple programming models or APIs, an important aspect is interoperability and standardisation of programming model, API and runtime as well as the composability of programming models (the capability of building new programming models out of existing programming model elements)

    b ) Exascale system software and management: Proposals should advance the state of the art in system software and management for node architectures that will be drastically more complex and their resource topology and heterogeneity will require OS and runtime enhancement, such as data aware scheduling. In the area of hardware abstraction, proposals should address run time handling of all types of resources (cores, bandwidth, logical and physical memory or storage) and controls, e.g. for optimised data coherency, consistency and data flow. For applications, proposals should address new multi-criteria resource allocation capabilities and interaction during task execution, with the aim to improve resilience, interactivity, power and efficiency. To cope with the exploding amount of data, the sequential analysis process (capture, store, analyse) is not sufficient; proposals should explore on-the-fly analysis methods offering reactivity, compute efficiency and availability. Graphical simulation interaction will require new real-time features; configuration and deployment tools will have to evolve to take into account the composability of software execution environments.

    c) Exascale I/O and storage in the presence of multiple tiers of data storage: proposals should address exascale I/O systems expected to have multiple tiers of data storage technologies, including non-volatile memory. Fine grain data access prioritisation of processes and applications sharing data in these tiers is one of the goals as well as prioritisation applied to file/object creates/deletes. Runtime layers should combine data replication with data layout transformations relevant for HPC, in order to meet the needs for improved performance and resiliency. It is also desirable for the I/O subsystem to adaptively provide optimal performance or reliability especially in the presence of millions of processes simultaneously doing I/O. It is critical that programming system interoperability and standardised APIs are achieved. On the fly data management supporting data processing, taking into account multi-tiered storage and involving real time in situ/in transit processing should be addressed.

    d) Supercomputing for Extreme Data and emerging HPC use modes: HPC architectures for real-time and in-situ data analytics are required to support the processing of large-scale and high velocity real-time data (e.g. sensor data, Internet of Things) together with large volumes of stored data (e.g. climate simulations, predictive models, etc.). The approaches should include support for real-time in-memory analysis of different data structures, direct processing of compressed data and appropriate benchmarking method for performance analysis. Interactive 3-D visualisation of large-scale data to allow users to explore large information spaces in 3-D and perform on-demand data analysis in real-time (e.g. large scale queries or analytics) should be addressed. Interactive supercomputing is required to execute complex workflows for urgent decision making in the field of critical clinical diagnostics, natural risks or spread of diseases; this implies adapting operational procedures of HPC infrastructures, developing efficient co-scheduling techniques or improving checkpoint/restart and extreme data management

    e) Mathematics and algorithms for extreme scale HPC systems and applications working with extreme data: Specific issues are quantification of uncertainties and noise, multi-scale, multi-physics and extreme data. Mathematical methods, numerical analysis, algorithms and software engineering for extreme parallelism should be addressed. Novel and disruptive algorithmic strategies should be explored to minimize data movement as well as the number of communication and synchronization instances in extreme computing. Parallel-in-time methods may be investigated to boost parallelism of simulation codes across a wide range of application domains. Taking into account data-related uncertainties is essential for the acceptance of numerical simulation in decision making; a unified European VVUQ (Verification Validation and Uncertainty Quantification) package for Exascale computing should be provided by improving methodologies and solving problems limiting usability for very large computations on many-core configurations; access to the VVUQ techniques for the HPC community should be facilitated by providing software that is ready for deployment on supercomputers.

    The Commission considers that proposals requesting a contribution from the EU between EUR 2 and 4 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts. Proposals should clearly indicate the subtopic which is their main focus. At least one project per subtopic will be funded.

    Expected Impact:

    Contribution to the realisation of the ETP4HPC Strategic Research Agenda, thus strengthened European research and industrial leadership in HPC technologies.
    Successful transition to practical exascale computing for the addressed specific element of the HPC stack.
    Covering important segments of the broader and/or emerging HPC markets, especially extreme-computing, emerging use modes and extreme-data HPC systems.
    Impact on standards bodies and other relevant international research programmes and frameworks.
    European excellence in mathematics and algorithms for extreme parallelism and extreme data applications to boost research and innovation in scientific areas such as physics, chemistry, biology, life sciences, materials, climate, geosciences, etc.

  • "Directional statistics in Signal and Image Processing" Roscoff, Brittany, 22-26th August 2016.


    bandeau dissip2016.JPG


    The first edition of the summer school "Directional statistics in Signal and Image Processing" (DISSIP) will be held in Roscoff, Brittany, from the 22nd to the 26th of August 2016. The School is sponsored by the CNRS and the Persyval Labex. The summer school is open to everyone (PhD students, post-docs, researchers, etc) and is designed to provide a comprehensive state of the art on directional statistics and their usage in the field of signal and image processing, and at large in data science. The program of the summer school contains courses (both theoretical and applied) together with lab sessions and discussion times.

    sponsor dissip.JPG

    The summer school will take place at the CNRS Marine Station conference center, located in Roscoff, Brittany.

    Registration deadline: June 30th, 201
    Summer school: August 22nd-26th, 2016

    Scientific Committee

    Nicolas Le Bihan (Gipsa-Lab) Pr. Peter E. Jupp (School of Mathematics and Statistics, University of St Andrews) Pr. Jonathan H. Manton (Department of Electrical and Electronic Engineering, The University of Melbourne)

    Organizing Committee

    Nicolas Le Bihan (Gipsa-Lab) Events Office Gipsa-Lab (Gipsa-Lab)

    Events Office
    11 Rue des mathematiques
    Domaine Universitaire
    38402 Saint Martin d'Heres Cedex
    email: Send an email

    Registration must be made through the CNRS "Colloque Axur" Application: Registration

    Note that the registration is in two parts. You must first make a pre-registration. After that you will receive, by email, all the informations to complete your registration online. Note that registration must be completed (including payment) by the 30th of June 2016
    Note as well that no reimbursement will be possible once your registration is complete.
    Registration fees

    Academics/professionals: 475 euros PhD students: 220 euros CNRS fellows: free of charges Summer school lecturers: free of charges

    Payment should be made preferentialy by credit card.
    Alternate option for payement should be by order form

    Please contact the Events office at Gipsa-lab if you have any question.

    Follow the link to obtain the provisional program.

  • Geo-Sci-Info

    Dates: June 27 - July 15, 2016


    Location: Université Claude Bernard Lyon 1, Lyon-Villeurbanne, France ↓ Access details

    Objectives: The thematic period aims to provide an overview of the current state of research in calculus of variations, optimal transportation theory, and geometric measure theory, from both the perspectives of theory and applications. The scope of the conference ranges from rigorous mathematical analysis to modeling, numerical analysis, and scientific computing for real world applications in image processing, computer vision, physics, material science, computer graphics, biology, or data science.

    Three events in three weeks :

    Week1: June 27-July 1 → First Summer School Week2: July 4-8 → International conference "Calculus of Variations, Geometric Measure Theory, Optimal Transportation: from Theory to Applications" Week3: July 11-15 → Second Summer School


    First Summer School (June 27-July 1, 2016)
    Start: Monday, June 27th at 1pm
    End: Friday, July 1st at 2.30pm

    Three 7-hours lectures by

    Dorin Bucur (U. Savoie)
    Shape optimization of spectral functionals ↓Abstract
    In these lectures, isoperimetric type inequalities involving the spectrum of the Laplace operator (with some boundary conditions) will be seen from a shape optimisation point of view. Depending on the boundary conditions, the analysis of those problems (existence of solution, regularity, qualitative properties) is either related to a free boundary problem of Alt-Caffarelli type, or to a free discontinuity problem. I will make an introduction to this topic and present recent results, with a focus on Robin boundary conditions. In particular I will detail a monotonicity formula which is the key point for the (Ahlfors) regularity of the optimal sets.

    Guido De Philippis (CNRS & ENS Lyon)
    The selection principle: the use of regularity theory in proving quantitative inequalities ↓Abstract
    I will first introduce the topic of quantitative inequalities and give some examples. Then I will present a general technique to derive them based on the regularity theory for solutions of variational problems. The course will be mainly focused on the (quantitative) isoperimetric inequality and on the (quantitative) Faber-Krahn inequality

    Filippo Santambrogio (U. Paris-Sud)
    Optimal transport, optimal curves, optimal flows ↓Abstract
    The course will consist in an introduction to optimal transport theory with a special attention to the comparison between Eulerian and Lagrangian point of views, and between statical and dynamical approaches. Optimal flow versions of some issues of the problem will also be presented, and this will lead at the end of the course to the study of some traffic equilibrium problems.
    The course will consist of four lectures, roughly divided as follows:
    1 . Basic theory of Optimal Transport
    _ The problems by Monge and Kantorovich.
    _ Convex duality and Kantorovich potentials.
    _ Existence of optimal maps (Brenier Theorem) for strictly convex costs.
    2 . Wasserstein distances
    _ Definitions of the distances W_p induced by optimal transport costs.
    _ The duality between W_1 and Lipschitz functions and the topology induced by the distances W_p.
    _ The continuity equation and the curves in the space W_p.
    3 . Curves of measures and geodesics in the Wasserstein space
    _ From measures on curves to curves of measures and back.
    _ Constant-speed geodesics in the Wasserstein space.
    _ The Benamou-Brenier dynamical formulation of optimal transport.
    4 . Minimal flows
    _ An Eulerian formulation of the Monge problem with cost |x-y| (p=1): the Beckmann problem.
    _ From measures on curves to vector flows and back.
    _ Extensions to traffic congestion models.

    Second Summer School (July 11-15, 2016)
    Start: Monday, July 11th at 9am
    End: Friday, July 15th at 6pm

    Three 7-hours lectures by

    Daniel Cremers (Munich)
    Variational Methods for Computer Vision ↓Abstract
    Variational methods are among the most classical and established methods to solve a multitude of problems arising in computer vision and image processing. Over the last years, they have evolved substantially, giving rise to some of the most powerful methods for optic flow estimation, image segmentation and 3D reconstruction, both in terms of accuracy and in terms of computational speed. In this tutorial, I will introduce the basic concepts of variational methods. I will then focus on problems of geometric optimization including image segmentation and 3D reconstruction. I will show how the regularization terms can be adapted to incorporate statistically learned knowledge about our world. Subsequently, I will discuss techniques of convex relaxation and functional lifting which allow to computing globally optimal or near-optimal solutions to respective energy minimization problems. Experimental results demonstrate that these spatially continuous approaches provide numerous advantages over spatially discrete (graph cut) formulations, in particular they are easily parallelized (lower runtime) and they do not suffer from metrication errors (better accuracy).

    Jérôme Darbon (CNRS & ENS Cachan)
    On Optimization Algorithms in Imaging Sciences and Hamilton-Jacobi equations ↓Abstract
    The course will consist of two parts
    1. Total variation minimization and maximal flows in graphs
    Applications to image processing
    Anisotropic mean curvature flow
    2. Optimization in image processing, Hamilton-Jacobi equations, and optimal control -----------------------------------------------------------------------------------------------------------------------------_

    Quentin Mérigot (CNRS & U. Paris-Dauphine)
    Computational optimal transport ↓Abstract
    Optimal transport has been used as a powerful theoretical tool to study partial differential equations, differential geometry and probability for a few decades. In comparison, its use in numerical applications is much more recent, not because of lack of interest but rather because of computational difficulties. The simplest discretization of the optimal transport problem lead to combinatorial optimization problems for which can only be solved with superquadratic cost. On the other hand, the partial differential equations arising from optimal transport are fully non-linear Monge-Ampère equations, for which there did not exist robust and efficient numerical solvers until recently. This course will present a variety of approaches to solve optimal transport and related problems, with applications in mind, such as:

    Entropic penalization and Wasserstein barycenters Benamou-Brenier algorithm, gradient flows and simulation of non-linear diffusion equations Monge-Ampère equation, computational geometry and convexity constraints Semi-discrete optimal transport and inverse problems in geometric optics Measure-preserving maps, optimal quantization and Euler's equation for incompressible fluids
    -----------------------------------------------------------------------------------------------------------------------------_ International conference "Calculus of Variations, Geometric Measure Theory, Optimal Transportation: from Theory to Applications" (July 4-8, 2016)
    Start: Monday, July 4th at 1pm
    End: Friday, July 8th at 2pm
    Conference program will be available soon

    Confirmed speakers
    Giovanni Alberti (Università di Pisa, Italy)
    Jean-François Aujol (Université de Bordeaux, France)
    Giovanni Bellettini (Università di Roma "Tor Vergata", Italy)
    Virginie Bonnaillie-Noël (École Normale Supérieure de Paris, France)
    Guy Bouchitté (Université du Sud-Toulon-Var, France)
    Blaise Bourdin (Lousiana State University, USA)
    Lia Bronsard (McMaster University, Canada)
    Michael Bronstein (Università della Svizzera Italiana, Switzerland)
    Almut Burchard (University of Toronto, Canada)
    Daniel Cremers (Technische Universität München, Germany)
    Qiang Du (Columbia University in the City of New York, USA)
    Selim Esedoḡlu (University of Michigan, USA)
    Ilaria Fragalà (Politecnico di Milano, Italy)
    Adriana Garroni (Università di Roma "La Sapienza", Italy)
    Young-Heon Kim (University of British Columbia, Canada)
    Jacques-Olivier Lachaud (Université de Savoie, France)
    Francesco Maggi (University of Texas at Austin, USA)
    Maks Ovsjanikov (École Polytechnique, France)
    Manuel Ritoré (Universidad de Granada, Spain)
    Dejan Slepčev (Carnegie Mellon University, USA)
    Jeremy Tyson (University of Illinois at Urbana-Champaign, USA)
    Bozhidar Velichkov (Université Grenoble Alpes, France)
    Max Wardetsky (Georg-August-Universität Göttingen, Germany)
    Stefan Wenger (University of Fribourg, Switzerland)
    Benedikt Wirth (Universität Münster, Germany)

    Accommodation for PhD students and postdoctoral young researchers
    Hotel accommodation (in shared double occupancy rooms) is offered to a maximal number of 60 PhD students or postdoctoral young researchers. Rooms are attributed in registration order. Use the pre-registration form below for application.

    Registration fees
    60 euros per week for permanent researchers
    30 euros per week for PhD students and postdoctoral researchers
    No registration fees for local researchers and local students
    Included with conference registration fees:

    hotel accommodation for a limited number of 60 PhD students or postdoctoral young researchers (rooms are attributed in registration order) daily coffee and refreshment breaks social events conference dinner

    Registration (deadline for payment: Monday, June 6)
    NB: Week 1 being labeled as a CNRS Thematic School, it benefits from a specific funding which requires a separate registration/payment procedure. We sincerely apologize for the total inconvenience of the whole registration process.

    Step 1: If you have not done it already, fill this pre-registration form Step 2: For Week 1 (First Summer School, partially funded by CNRS), register and pay here→ Registration/Payment for Week #1 Step 3: [[ Important ]] Log out from Week 1 registration process using this link Step 4: For Weeks 2 and/or 3 (International Conference and/or Second Summer school), register and pay here→ Registration/Payment for Weeks #2 and/or #3 Step 5: [[ Important ]] Log out from Weeks 2-3 registration process using this link Step 6: Congratulations, you did it! (and many thanks for your patience...)

    Scientific Committee
    Lorenzo Brasco
    Dorin Bucur
    Antonin Chambolle
    Thierry De Pauw
    Guy David
    Vincent Feuvrier
    Antoine Lemenant
    Quentin Mérigot
    Benoit Merlet
    Vincent Millot
    Laurent Moonens
    Edouard Oudet
    Olivier Pantz
    Séverine Rigot
    Filippo Santambrogio

    Organizing Committee
    Elie Bretin
    Sarah Delcourte
    Simon Masnou
    Hervé Pajot

    For any questions about the thematic period, please contact us at this email address.

  • 16th June 2016 - University Paris 13


    Journée d’exposés en "Combinatoire, Opérades et Probabilités"


    Université Paris 13 (salle B405 du LAGA)
    Indications pour se rendre à Paris 13

    Programme :

    10h-11h : "Approche homotopique des probabilités libre d’après Drummond-Cole—Park—Terilla" [Bruno Vallette, Université Paris 13] 11h30-12h30 : "Des séries en arbres aux séries de type Dirichlet" [Frédéric Chapoton, Université de Strasbourg] Déjeuner 14h-15h : "Sur une généralisation des probabilités libres en matrices aléatoires" [Camille Male, Université Paris Descartes] 15h30-16h30 : "Cumulants libres non-commutatifs" [Jean-Yves Thibon, Université Paris-Est Marne-la-Vallée]

    Résumés :

    "Approche homotopique des probabilités non-commutatives d’après Drummond-Cole—Park—Terilla" [Bruno Vallette, Université Paris 13]
    Le but de cet exposé est d’offrir un résume aussi élémentaire que possible de l’approche homotopique proposée par Drummond-Cole—Park—Terilla des cumulants apparaissant en probabilité non-commutative. Le point principal consiste à interpréter ces derniers comme des infini-morphismes des algèbres à homotopie près sur certaines opérades de Koszul. On retrouve alors toutes les formules de cumulants des probabilités non-commutative à l’aide des formules d’inversion des infini-isomorphismes et de l’action du groupe de jauge de la théorie de la déformation des algèbres à homotopie près. Aucun prérequis n'est nécessaire pour suivre cet exposé; toutes les notions seront rappelées, ce qui permettra aussi de servir d’introduction aux autres exposés de la journée. "Des séries en arbres aux séries de type Dirichlet" [Frédéric Chapoton, Université de Strasbourg]
    Je présenterai deux séries en arbres particulières qui jouent un rôle comparable à celui de l'exponentielle et du logarithme, et qui apparaissent naturellement en analyse numérique. J'expliquerai ensuite comment une déformation du "logarithme en arbres" est motivée par certaines considérations algébriques. Je donnerai une description des coefficients de ce q-logarithme en arbres
    en termes de certains polytopes, et éventuellement la relation entre certains de ces coefficients et des séries de type Dirichlet. "Sur une généralisation des probabilités libres en matrices aléatoires” : [Camille Male, Université Paris Descartes]
    Certains modèles de matrices aléatoires hermitiennes ont la propriété de se diagonaliser dans une base qui n'est pas "asymptotiquement uniformément distribuée" lorsque la taille des matrices tend vers l'infini, contrairement aux matrices classiques de Wigner ou de Wishart. En conséquence, les distributions asymptotiques de valeurs propres de polynômes hermitiens en de telles matrices indépendantes diffèrent parfois des prédictions données par les probabilités libres.
    Un espace de probabilités non commutatif étant une algèbre munie d'une forme linéaire, nous verrons qu'il est possible grace aux opérades d'introduire une notion de variable non commutative plus riche qui permet de calculer les moments de ces distributions dans des cas variés. Les résultats sont exprimés en termes d'une notion d'indépendance qui unifie l'indépendances classique (tensorielle), l'indépendance libre de Voiculescu et encode également d'autres relations. "Cumulants libres non-commutatifs” [Jean-Yves Thibon, Université Paris-Est Marne-la-Vallée]
    L'équation fonctionnelle définissant les cumulants libres dans le cas d’une seule variable aléatoire peut être relevée successivement à l'algèbre de Faà di Bruno non-commutative, puis au groupe d'une opérade libre. La solution de cette équation prend en compte le cas d'une mesure à valeurs opératorielles, et redonne la formule de Speicher dans le cas d'une mesure scalaire. On peut aussi interpréter cette équation comme une généralisation de celle d'Ebrahimi-Fard et Patras.

    Tous les détails de cette rencontre sont disponibles à l’adresse suivante :

    Deux pauses cafés et le déjeuner sont prévus. Si vous pensez venir, merci de nous prévenir par courriel à vallette [ at]
    N’oubliez pas de venir avec une copie de ce message ou de la page de la rencontre pour pouvoir entrer dans le campus.

    Organisateurs :
    Eric Hoffbeck et Bruno Vallette
    Soutiens :
    Pole Mathstic Paris13 et projet ANR SAT

  • 3rd International Electronic and Flipped Conference on Entropy and Its Applications - 1-10 nov 2016


    Welcome from the Chair of the Forum


    You are cordially invited to participate in the 3rd International Electronic and Flipped Conference on Entropy and Its Applications. This event is designed to bring together researchers working in the field to present and discuss their recent contributions, without the need to travel.

    The field of entropy-related research has been particularly fruitful in the past few decades, and continues to produce important results in a vast range of fields spanning all branches of the sciences, engineering, social sciences, economics / finance and the humanities. We welcome your contributions on theoretical insights into the entropy concept and on information theory, and on practical applications of the maximum entropy method in any field.

    This year, we will also make every effort to facilitate interaction and the exchange of ideas during the conference period. All presenters will be asked to prepare an audio or video of their presentation. This will be uploaded onto the conference platform in advance, for viewing by others during the conference period - possibly one of the first “flipped mode” conferences in the world! We will also schedule an online discussion period for each presentation. We will announce further details of the technological requirements and schedule in due course.

    The conference will be organized into six sessions, which reflect the inter-disciplinary nature of entropy and its applications:

    Section A
    Physics and Engineering: Thermodynamics, Statistical Mechanics, the Second Law of Thermodynamics, Reversibility, Quantum Mechanics, Black Hole Physics, Maximum Entropy Methods, Maximum Entropy Production, Evolution of the Universe Section B
    Information Theory: Shannon Entropy, Kullback-Leibler Divergence, Channel Capacity, Alternative Entropies, and Applications Section C
    Complex Systems: Self-Organization, Chaos and Nonlinear Dynamics, Simplicity and Complexity, Networks, Symmetry Breaking, Similarity Section D
    Chemistry and Biology: Chemical Networks, Energy, Enthalpy, Maximum Entropy Methods, Biological Networks, Evolution, DNA and RNA, Diversity Section E
    Machine Learning and Systems Theory: Artificial Intelligence, Neural Networks, Cybernetics, Robotics, Man–Machine Interfaces, Causality Section P
    Posters: In this section, posters can be presented stand-alone, i.e., without an accompanying proceedings paper or conference presentation. Posters will be available online on this website during and after the e-conference. However, posters will not be added to the proceedings of the conference.

    Accepted papers will be published in the proceedings of the conference, and selected/extended papers will be considered for publication in Entropy with a 20% discount off the APC. Entropy is an open access publication journal of MDPI in the field of entropy and information theory.

    Important Dates:
    Abstract Submission: 18 September 2016
    Notification of Acceptance: 30 September 2016
    Submission of Full Paper and Poster/Presentation: 20 October 2016
    Conference Open: 1-10 November 2016

    Please do not hesitate to contact us if you have questions. It is my pleasure to welcome you to the Third e-Conference on Entropy!

    Best regards,
    Robert Niven
    The University of New South Wales, Canberra, Australia
    Google Scholar
    Contact me on ResearchGate!

    Robert Niven has a BSc (Hons) in chemistry and geology and a PhD in civil and environmental engineering, both from The University of New South Wales, Australia. He is a long-standing Editorial Board Member and reviewer for Entropy (since 2007), and was Chair of the MaxEnt 2013 conference in Canberra, Australia. Over the past decade, Dr Niven has developed new theoretical perspectives into the entropy concept based on combinatorial reasoning, and has also pioneered new applications of the maximum entropy method for the analysis of dissipative non-equilibrium systems, fluid flow systems, dynamical systems and flow networks. This research has been recognized by a number of international fellowships and awards, including Japan Society for the Promotion of Science Invitation Fellowship (2006), Marie Curie Incoming International Fellowship, Denmark (14 months, 2007‑08); Endeavour Executive Award (2010), Isaac Newton Institute Visiting Fellowship (2010), CNRS Fellowship, France (2011) and Invited Professor, Institut PPrime / Région Poitou-Charentes, France (11 months, 2014). His most recent research interests include the maximum entropy analysis of turbulent flow systems, graphical systems and urban flow networks, including distributed power generation, water distribution and vehicular systems

    Entropy Journal

  • Geo-Sci-Info

    Cher(e)s Collègues,

    Nous avons le plaisir d'annoncer le séminaire suivant:

    Vendredi 24 juin, 15h00, École polytechnique, salle de conférence du CPHT (rez de chaussée de l'aile 0 des laboratoires)

    Rajendra Bhatia (Indian Statistical Institute, New Delhi)

    Titre: Bures-Wasserstein distance between positive definite matrices

    Résumé: We will discuss from the perspective of matrix analysis an interesting metric on the space of positive definite matrices, that has connections to several areas like quantum information, statistics, optimal transport and Riemannian geometry.

    En raison des mesures de sécurité, un document d'identité pourra vous etre demandé pour accéder à l'École. Nous vous recommandons aussi d'avoir avec vous une copie de cette annonce.

    Bien cordialement
    Les organisateurs

    Stéphane Gaubert (INRIA et CMAP)
    Éric Goubault (LIX)

    PS. Pour venir à l'École polytechnique:

    L'aile 0 des laboratoires est indiquée par le numéro 0 sur le plan suivant:
    Nous recommandons d'accéder à l'aile 0 par l'entrée donnant sur le parking des laboratoires: cette entrée fait face à la salle de conférence.

  • National University of Singapore - 4 - 31 July 2016


    Schedule and Abstracts PDF

    Tutorial on Manifolds of Diffeomorphisms, EPDiff
    Martin Bauer, University of Vienna, Austria

    Riemannian geometries on the space of curves I Riemannian geometries on the space of curves II
    Abstract (1) and (2): The space of curves is of importance in the field of shape analysis. I will provide
    an overview of various Riemannian metrics that can be defined thereon, and what is known about the
    properties of these metrics. I will put particular emphasis on the induced geodesic distance, the
    geodesic equation and its well-posedness, geodesic and metric completeness and properties of the
    curvature. In addition I will present selected numerical examples illustrating the behaviour of these
    metrics. Right invariant metrics on the diffeomorphism group
    The interest in right invariant metrics on the diffeomorphism group is fuelled by its relations to
    hydrodynamics. Arnold noted in 1966 that Euler's equations, which govern the motion of ideal,
    incompressible fluids, can be interpreted as geodesic equations on the group of volume preserving
    diffeomorphisms with respect to a suitable Riemannian metric. Since then other PDEs arising in
    physics have been interpreted as geodesic equations on the diffeomorphism group or related spaces.
    Examples include Burgers' equation, the KdV and Camassa-Holm equations or the Hunter-Saxton
    Another important motivation for the study of the diffeomorphism group can be found in its
    appearance in the field of computational anatomy and image matching: the space of medical images
    is acted upon by the diffeomorphism group and differences between images are encoded by
    diffeomorphisms in the spirit of Grenander's pattern theory. The study of anatomical shapes can be
    thus reduced to the study of the diffeomorphism group.
    Using these observations as a starting point, I will consider the class of Sobolev type metrics on the
    diffeomorphism group of a general manifold M. I will discuss the local and global well-posedness of
    the corresponding geodesic equation, study the induced geodesic distance and present selected
    numerical examples of minimizing geodesics. The space of densities
    I will discuss various Riemannian metrics on the space of densities. Among them is the Fisher--Rao
    metric, which is of importance in the field of information geometry. Restricted to finite-dimensional
    submanifolds, so-called statistical manifolds, it is called Fisher's information metric. The Fisher--Rao
    metric has the property that it is invariant under the action of the diffeomorphism group. I will show,
    that on a closed manifold of dimension greater than one, every smooth weak Riemannian metric on
    the space of smooth positive probability densities, that is invariant under the action of the
    diffeomorphism group, is a multiple of the Fisher--Rao metric.

    Tutorial on Manifolds of Diffeomorphisms, EPDiff
    Martins Bruveris, Brunel University London, UK

    Lecture I - Mapping spaces as manifolds
    This lecture will give an introduction to differential geometry in infinite dimensions. The main objects of
    shape analysis - the diffeomorphism group, the spaces of curves, surfaces, densities - can all be
    modelled as infinite-dimensional manifolds.
    Lecture II - Riemannian geometry in infinite dimensions
    Parts of Riemannian geometry generalise easily from finite to infinite dimensions. These include the
    definition of metric, covariant derivative, geodesic equations and curvature. But there are also
    qualitative differences, in particular with the distinction between strong and weak Riemannian metrics.
    This lecture will show some of the purely behaviour that can be encountered in infinite dimensions.
    Lectures III and IV - Riemannian metrics induced by the diffeomorphism group
    The purpose of these lectures is to explore the geometry of Riemannian metrics on the space of
    curves and landmarks that are induced by the action of the diffeomorphism group. These metrics
    correspond to exact matching of curves and landmarks via LDDMM. We will look at the induced
    metrics, geodesic equations and the geodesic distance.

    Introduction to the Differential Geometry
    Joan Alexis Glaunès (Université Paris Descartes, France) and
    Sergey Kushnarev (Singapore University of Technology and Design)

    Definition of a manifold, Tangent Vectors and Tangents Spaces, Pushforwards, Vector Fields. Tangent bundle and a Cotangent Bundle, Pullbacks, Tensors, Differential Forms. Submersions, Immersions, Embeddings, Submanifolds (Embedded, Immersed) Integral Curves and Flows, Lie Derivatives. Riemannian Metrics. Connections. Riemannian Geodesics and Distance (exp map, normal coordinates, geodesics and minimizing
    distances). Curvature.

    Diffeomorphic Models and Matching Problems in the Discrete Case
    Joan Alexis Glaunès, Université Paris Descartes, France
    This talk will be an introduction and on overview of the framework of diffeomorphic mappings
    (LDDMM) for estimating deformations between shapes, and its formulation for discrete problems via
    reproducing kernels. I will present the classical construction of the group of diffeomorphisms, and
    explain how by considering different types of actions on this group, it can be used to estimate
    deformations between different types of geometric data: images, points, surfaces, etc. I will show
    some experiments and studies to illustrate.

    Geodesic Equations and Shooting Algorithms for Matching and Template Estimation
    Joan Alexis Glaunès, Université Paris Descartes, France
    In this talk I will explain the link between diffeomorphic mappings and shape spaces, i.e. Riemannian
    metrics on sets of shapes. I will explain how the metric on the group of diffeomorphisms induces a
    metric on the space of shapes, and detail the geodesic equations in the finite dimensional case
    (manifold of landmarks), which is the case in use in practice for many problems once data has been
    discretised. I will present different algorithms which are based on these equations (geodesic shooting
    algorithms): matching, template estimation, geodesic regression, and explain how all this can be
    actually implemented.

    Models for Diffeomorphic Mappings between Submanifolds: measures, currents, varifolds
    Joan Alexis Glaunès, Université Paris Descartes, France
    This talk will focus on some models for defining data attachment terms for matching problems
    between submanifolds (curves or surfaces) which are widely used for diffeomorphic mappings. These
    are all based on the same idea of defining dual RKHS spaces and using the corresponding norm as a
    data attachment term between shapes. This uses mathematical concepts such as currents or
    varifolds, which come from geometric measure theory and which I will introduce. I will present both
    continuous and discrete forms of these models, and show some outputs of algorithms

    Reproducing Kernels in the Vectorial Case
    Joan Alexis Glaunès, Université Paris Descartes, France
    The theory of reproducing kernels and Reproducing Kernel Hilbert Spaces (RKHS) is extensively
    used in the discrete formulation of the LDDMM setting, and in corresponding algorithms. It is also a
    fundamental concept in other areas, such as statistical learning. I will present some basic concepts of
    this theory in the general case of RKHS of vector fields, and explain how this theory can be used for
    interpolation problems, and how it is linked to the LDDMM setting. I will also present shortly a recent
    study about translation and rotation invariant kernels, which allows in particular to consider spaces of
    divergence free or irrotational vector fields for deformation analysis.

    Lie Groups and Lie Group Actions
    Richard Hartley, Australian National University, Australia
    I will talk about Lie groups and Lie group actions on manifolds, with particular consideration for
    applications in Computer Vision. Lie groups play a significant role in Computer Vision, particularly
    groups such as SO(3), the group of 3-D rotations, SE(3), the group of Euclidean motions, and
    PGL(2,R) and PGL(3, R), the groups of 2 and 3-dimensional projective transformations. In addition,
    Lie group actions on such manifolds as the Stiefel manifolds (yielding Grassman manifolds as a space
    of orbits) and the action of O(2) on SO(3) x SO(3), yielding the Essential manifold, as well as Shape
    manifolds as an orbit space of an action of similarity transforms, are common examples where Lie
    group actions give rise to Riemannian manifold structures. Applications are in the areas of Lie group
    tracking, averaging (for instance rotation averaging), and kernels on manifolds such as shape
    manifolds and Grassman manifolds, all with important applications in computer vision and robotic

    Tutorial on Wavelets
    Hui Ji, National University of Singapore
    This lecture focuses on the introduction to wavelet frame and its applications in imaging and vision.
    The goal to expose audience to important topics in wavelet frames with strong relevance to visual
    data processing, in particular image processing/analysis. The audience will also learn how to apply
    these methods to solve real problems in imaging and vision. The lecture is an inter-disciplinary one
    that emphasizes both rigorous treatment in mathematics and motivations from real-world applications.

  • CIRM Workshop: SIGNAL, IMAGE, GEOMETRY, MODELLING, APPROXIMATION + session Claude Shannon- October 31-November 4th, 2016 - Marseille, France


    Workshop SIGMA'2016


    Important: all the participants are hosted in the CIRM.

    SIGMA'2016 aims at gathering specialists of the following main domains: Signal-Image, Geometric Modelling, Computational Geometry, Approximation. The workshop will be composed of invited plenary talks given by experts in these fields and contributed presentations.

    SIGMA'2016 which will take place from Monday, October 31st, to Friday, November 4th, 2016, in the International Center for Mathematical Meetings (CIRM), located on the Campus of Luminy, Marseille, France. It is organized by SMAI-SIGMA one of the activity groups of the SMAI (French equivalent of the SIAM).


    October 31st to November 4th, 2016


    CIRM, Marseille

    Plenary Speakers

    Rachid Ait-Haddou (Osaka) Pierre Alliez (INRIA) Jean-Daniel Boissonnat (INRIA) Bernardo De la Calle (Madrid) Antonin Chambolle (CNRS and Polytechnique) Jalal Fadili (Caen) Mario Figueiredo (Lisboa) Daniel Kressner (EPF Lausanne) Arno Kuijlaars (Leuven) Gitta Kuttyniok (Berlin) Juliette Leblond (INRIA Sophia) Carla Manni (Rome) Konstantin Mischaikow (Rutgers University) Anthony Nouy (Nantes) Miguel Pinar (Granada) Christoph Schnorr (Heidelberg) Christian Sohler (Dortmund) Gabriele Steidl (Kaiserslautern) Georg Umlauf (Konstanz) Holger Wendland (Oxford)

    Scientific committee

    Albert Cohen (Paris 6) Olivier Gibaru (ENSAM) Christian Gout (Rouen) Quentin Mérigot (CNRS and Paris-Dauphine) Eric Nyiri (ENSAM) Valérie Perrier (Grenoble)

    Organizing committee

    Bernhard Beckermann (Lille 1) Frédéric Chazal (INRIA) Tom Lyche (Oslo) Marie-Laurence Mazure (Grenoble) Gabriel Peyré (CNRS and Paris-Dauphine, France)

    Pre-registration: free but mandatory

    Register online here.


    [not yet available]


    [not yet available]

    Friday afternoon dedicated to Claude Shannon (in French)

    A l'occasion du centenaire de la naissance de Claude Shannon, le groupe SIGMA de la SMAI organise le Vendredi 4 Novembre après midi au CIRM une après midi d'exposés grand public autour de l'oeuvre scientifique de Claude Shannon, de la théorie de l'information et de ses applications.

    14:00-14:30 : Caroline Chaux (CNRS et Univ. Marseille) : l'échantillonnage
    14:30-15:00 : Gabriel Peyré (CNRS et Univ. Paris-Dauphine): la compression de données
    Pause café
    15:30-16:00 : Christophe Ritzenthaler (Univ. Rennes) : les codes correcteurs
    16:00-16:30 : Jalal Fadili (ENSICaen): l'échantillonnage compressé


    [not yet available]


    The CIRM is located in a nice place, close to some of the famous "Calanques de Marseille", beautiful typical creeks. There will be no talks on Wednesday afternoon to give the participants an opportunity to enjoy the surroundings. More details about how to get there can be found on the web site of the CIRM.

    The conference includes breakfasts, lunches and diners free of charge from Sunday evening to Friday noon. Transportation is left at the charge of the participants.

    There is only a limited number of single and double hotel rooms at the CIRM (from Sunday evening to Friday morning). Those rooms are free of charge. People who will register late may not obtain one of them, and will have to book an hotel room outside the CIRM on their own. We thus advise you to register early.



  • Geo-Sci-Info

    General International conferences on distances


    General international conferences on distances were:

    1992: Distancia, org. by Le Calve in Rennes, France
    Proc. published by Universite de Bretagne, 1992

    1994: Discrete Metric Spaces, org. by W.Deuber and M.Deza in Bielefeld, Germany.
    Proc. European Journal of Combinatorics, Special Issue of
    Discrete Metric Spaces, 17-2,3 (1996)

    1996: Discrete Metric Spaces II, org. by W.Deuber and M.Deza in Lyon, France.
    Proc. Discrete Mathematics 192,1-3 (1998)

    1998: Discrete Metric Spaces III, org. by M.Deza in CIRM, France
    Proc. European Journal of Combinatorics, Special Issue of
    Discrete Metric Spacesâ, 21-6 (2000)

    2012: Mathematics of Distances and Applications, org. by M.Deza, M.Petitjean and
    K.Markov in Varna, Bulgaria
    Proc. were published as a book

    2013: Distance Geometry and Applications - DGA'2013, org. by C.Lavor, A.Mucherino et al.
    in Manaus, Brazil
    Proc. in Special Issue of Discrete Appl. Math. 2014

    2013: GSI2013 - Geometric Science of Information
    , Paris, France.
    SESSION: Discrete Metric Spaces

    2014: Many Faces of Distances, workshop, org. by C.Lavor, M.Firer et el.
    in Campinas, Brazil

  • Day Conference - Institut Henri Poincaré - 14 Sept. 2016


    Bandeau duhem.jpg


    Pierre Duhem (1861-1916) et ses contemporains
    Institut Henri Poincaré, 14 Septembre 2016
    Amphithéâtre Hermite
    organisée par Hervé Le Ferrand (Dijon) - Laurent Mazliak (Paris)


    9h30 Accueil-Présentation
    9h45-10h30 Nicolas Wipf (Metz) « Le jeune Duhem: formation et controverses »
    10h30-11h15 Antonietta Demuro - Rossana Tazzioli (Lille) « Les premières recherches en hydrodynamique à Lille »
    11h30-12h15 Jean-François Stoffel (Bruxelles) « Duhem à travers sa correspondance : quelques surprises et quelques confirmations »
    12h15-13h00 Claude Bardos (Paris) « Pierre Duhem et Jacques Hadamard à Bordeaux »


    14h30-15h15 Stefano Bordoni (Bologne) « Pierre Duhem et la thermodynamique »
    15h15-16h00 Anastasios Brenner (Montpellier) « Pierre Duhem épistémologue »
    16h00-16h45 Cédric Chandelier (Aix-Marseille) « Pierre Duhem historien des sciences »
    16h45-17h30 Discussion générale

    Pour tout renseignement : laurent.mazliak [ at ]

    Résumés des conférences

    Claude Bardos (laboratoire Jacques Louis Lions, Université Pierre et Marie Curie)
    « Pierre Duhem et Jacques Hadamard à Bordeaux : de l’amitié à l’éclosion de la théorie moderne des équations aux dérivées partielles »
    Pour cet exposé je dispose avant tout de deux éléments. D’une part une bonne connaissance de l’œuvre d’Hadamard sur les équations aux dérivées partielles et d’autre part de l’excellent livre de Mazya et Shaposhnikova également sur Hadamard. Je me propose donc de montrer comment les travaux de Paul Duhem qui se concrétisent par exemple dans le livre Hydrodynamique acoustique , élasticité , acoustique (Hermann, Paris, 1891) ont pu dans la période où ils étaient tous les deux à Bordeaux, conduire Jacques Hadamard a élaborer des concepts qui motiveront les recherches d’au moins trois générations de mathématiciens. Stefano Bordoni (Scuola di Farmacia, Biotecnologie e Scienze motorie, Université de Bologne)
    « Pierre Duhem et la thermodynamique »
    Je me concentrerai sur la thermodynamique généralisée de Duhem ou, si vous préférez, sa mécanique généralisée ou Energétique. Duhem a toujours mentionné les savants qui l’ont précédé dans la recherche d’une théorie physique générale : d’abord Rudolf Clausius, et ensuite François Massieu, Joshia Willard Gibbs, Hermann von Helmholtz, Arthur von Oettingen … Il a aussi reconnu le rôle joué par Lagrange dans le développement d’une physique qui pouvait se libérer des modèles mécaniques microscopiques. Je donc voudrais présenter Duhem comme point d’arrivée d’une tradition : je me concentrerai sur les premières étapes de Duhem thermodynamicien, sur les années qui vont de 1892 à 1896, pour retracer quelques influences et pour souligner les nouveautés.
    En 1894, dans la troisième partie de son « Commentaire aux principes de la Thermodynamique » il étonna probablement les lecteurs en raison de la référence à une interprétation aristotélicienne du mot «mouvement»: non seulement le mouvement était considéré comme un processus cinématique, mais comme une transformation en général [Duhem 1894a, p. 222]. Pour sa thermodynamique généralisée, Duhem choisit une généralisation du lexique mécanique traditionnel. L’équilibre thermique d’un système physique était perturbé par des actions qui étaient la généralisation du frottement et de la viscosité mécaniques.. Les résistances généralisées lui permettaient de réinterpréter l'entropie [Duhem 1894a, pp. 223-4 and 229] et de mettre en place une nouvelle physique généralisée qui prétendait avoir l’ampleur de la philosophie naturelle d’Aristote. En 1896, dans le livre Théorie thermodynamique de la viscosité, du frottement et des faux équilibres chimiques, Duhem essaya de construire une structure mathématique aussi générale que souple, qui pourrait s'adapter aux particularités des systèmes spécifiques, et pourrait être progressivement élargie afin de rendre compte de phénomènes d'une complexité croissante.
    Les équations générales contenaient aussi bien les termes d'inertie que deux termes dissipatifs. Quand il laissait tomber les termes de dissipation, une réinterprétation de la mécanique traditionnelle émergeait. Quand il laissait tomber les termes d'inertie, certaines simplifications mathématiques le conduisaient à une nouvelle mécanique des processus chimiques explosifs [Duhem 1896, pp. 128-131]. Une structure mathématique flexible pouvait inclure à la fois la science antique et moderne, la mécanique classique et une nouvelle mécanique chimique qui pouvait être considérée comme une réinterprétation de la philosophie naturelle d'Aristote [Duhem 1896, p. 205]. Anastasios Brenner (C.R.I.S.E.S., Université Montpellier 3)
    « Pierre Duhem épistémologue »
    La réflexion philosophique de Duhem est suscitée par la crise qui secoue la physique au tournant du XXe siècle : la constitution d’une thermodynamique indépendante de la mécanique. Il fait remonter l’ébauche de sa doctrine à un défi : présenter la thermodynamique de façon rigoureuse et intelligible. Les concepts et les principes de cette branche de la physique se distinguent par leur généralité et leur abstraction. Duhem se donne alors pour tâche de « reprendre jusqu’en ses fondements l’analyse de la méthode par laquelle se peut développer la théorie physique ». Ses écrits philosophiques visent à fournir une explication et une justification de cette démarche. Duhem est conduit à rejeter une conception empiriste naïve ; c’est le sens de sa critique de la méthode newtonienne et de l’expérience cruciale. Les grands principes de la physique ne peuvent plus être présentés comme le résultat direct de la raison commune ou de l’expérience ordinaire. Ce sont des conventions, qui manifestent le libre choix du théoricien. Cette affirmation fait écho à la position énoncée simultanément par Poincaré au sujet des hypothèses de la géométrie. La convergence entre les deux savants ne manque pas de frapper leurs contemporains, ouvrant la voie à une doctrine épistémologique originale.
    Il ne s’agit pas d’être conduit au scepticisme, et l’œuvre duhémienne se déploie comme une tentative d’élucidation : l’intégration de la théorie et de l’expérience, l’évolution des concepts ainsi que le concours de valeurs rationnelles enclenchent un mouvement lent et subreptice mais indéniable. Selon Duhem la représentation que procure la théorie aboutit à une organisation des lois, à une classification. Au cours du temps, les classifications sont améliorées ; elles deviennent de plus en plus naturelles. Les exigences d’exactitude, de cohérence et de puissance prédictive fournissent une justification rationnelle du choix du scientifique. Cédric Chandelier (C.E.P.E.R.C., Université d'Aix-Marseille)
    « Pierre Duhem historien des sciences »
    Les théories physiques évoluent, d’après Pierre Duhem, vers une forme parfaite qu’elles n’atteindront jamais. C’est dans cette tension vers une « classification naturelle » que réside ce qui distingue l’épistémologie duhémienne du conventionnalisme. Duhem, pas plus que Poincaré, ne renonce au nom de la liberté à la valeur ontologique du savoir positif, et l’histoire joue un rôle crucial dans la reconnaissance, au cœur de la science, d’une aspiration qui la dépasse. Qu’aucune théorie ne puisse être infirmée par l’expérience ne la rend pas arbitraire vis-à-vis des faits empiriques : le but du physicien selon Duhem est la représentation ordonnée des lois expérimentales ; et la recherche scientifique de l’harmonie de ces lois trouve sa source dans une croyance de métaphysicien. En dépit du mur indépassable que l’épistémologue place entre la méthode positive et l’affirmation métaphysique, l’historien des sciences prend acte de cette affirmation, par laquelle le savant transgresse l’infranchissable. Et si Duhem s’en tient explicitement au schisme de l’âme entre corps et conscience, la liberté qu’il prend vis-à-vis de sa propre doctrine permet de mesurer l’erreur qu’il y a à réduire la physique du croyant au dogme catholique. L’histoire des sciences, chez Duhem, ne peut être cloisonnée de l’histoire des cosmologies : la « preuve par analogie », qui fait du mouvement de la physique théorique une métaphore de celui de la foi, n’a certes pas la valeur contraignante d’une démonstration logique, mais de même que l’expérience peut conduire sans nécessité à la préférence d’une théorie plus globale, rien n’empêche de franchir l’indémontrable en transgressant allégoriquement le sens positif de l’histoire. C’est ce que Duhem fait lui-même en trouvant dans la thermodynamique une image de la cosmologie péripatéticienne. Antonietta Demuro, Rossana Tazzioli (Laboratoire Painlevé, Université Lille 1)
    « Les premières recherches en hydrodynamique à Lille»
    Pierre Duhem a passé à Lille les premières années de sa carrière académique, de 1887 à 1893. Les sujets de ses cours donnés à l'Université de Lille concernent l'hydrodynamique, l'électricité, le magnétisme, la thermodynamiuque, et l'optique. Le but de notre exposé est de contextualiser les premières recherches de Duhem en hydrodynamiques pas seulement au point de vue scientifique et institutionnel, mais aussi par rapport à ses enseignements, à l'intéraction avec ses collègues et à son engagement vis à vis des élèves. Jean-François Stoffel (Bruxelles)
    « Duhem à travers sa correspondance : quelques surprises et quelques confirmations »
    Le Fonds Pierre Duhem de l’Institut de France conserve la correspondance, essentiellement passive, professionnelle (2.961 lettres échangées avec 542 correspondants de 17 nationalités différentes) et personnelle (1.049 lettres adressées à sa fille Hélène) de notre savant. Cette mine d’informations permet de préciser sa biographie intellectuelle (par ex. les raisons de son maintien loin de la capitale) et de reconstituer son véritable réseau de relations, souvent éloigné de celui qu’on pouvait imaginer. Il apparaît en effet que certains auteurs, dont le nom n’apparaît que très occasionnellement sous la plume de notre penseur, voire pas du tout (par ex. M. Blondel, B. Lacome ou P. Mansion), ont, en fait, particulièrement stimulé sa pensée, quand d’autres, qui paraissaient sembler intellectuellement proches de lui (par ex. J. Bulliot, le destinataire d’une célèbre lettre encore souvent rééditée), ne le sont guère et attirent, de sa part, un jugement pour le moins sévère. C’est dire si les informations contenues dans cette correspondance, bien que disparates et souvent difficiles à exploiter, sont en fait irremplaçables. Nicolas Wipf (Lycée Fabert, Metz)
    « Le jeune Duhem: formation et controverses »
    En suivant le parcours du jeune Duhem, des bancs du collège à son entrée à l’Université, nous verrons apparaître les prémisses de l’œuvre d’un physicien doué et ambitieux, d’un épistémologue ancré dans ses convictions et d’un homme au caractère parfois ombrageux.
    Duhem intègre le Collège Stanislas en 1872, un établissement au sein duquel il retrouve les valeurs catholiques et conservatrices inculquées par ses parents. Excellent élève, il s’initie très tôt aux sciences physiques grâce à son professeur Jules Moutier : « c’est ce maître qui fit germer en nous l’admiration pour la théorie physique et le désir de contribuer à son progrès ». La lecture de travaux récents publiés par Gibbs et Helmholtz l’oriente alors rapidement vers son futur programme de recherche : l’élaboration d’une thermodynamique générale.
    Premier de promotion à l’Ecole Normale Supérieure (1882 – 1887), il présente dès 1884 sa propre théorie du « potentiel thermodynamique » comme thèse de physique mathématique. La commission chargée d’évaluer ce travail considère toutefois que celui-ci n’est « pas de nature à être soutenu comme thèse devant la Faculté des Sciences de Paris ». Ce refus s’explique-t-il simplement par le manque d’expérience de l’impétueux normalien ? Ne serait-ce pas plutôt lié au fait que Duhem y critique vivement la théorie défendue par Berthelot, figure influente de la chimie française et homme politique de premier ordre durant la Troisième République ?
    Nommé à l’Université de Lille en 1887, Duhem espère que cette affectation précède un retour rapide à Paris… Mais il passera finalement l’ensemble de sa carrière universitaire en province (après un départ rocambolesque de Lille en 1893 et un court passage à Rennes, il finira sa carrière à Bordeaux). Selon lui, les « potentats » de la communauté scientifique, Berthelot en tête, ont tout fait pour « lui barre[r] la route de Paris ».

  • 11-12th October - INRIA Paris Research Center


    CAVALIERI Workshop on Optimization and Optimal Transport in Imaging


    Dates: 11th to 12th October
    Location: Salle Jacques-Louis Lions 2 at INRIA Paris Research Center



    Claire Boyer, LSTA, UPMC Nicolas Bonneel, Liris, Université Claude-Bernard Lyon 1 Elsa Cazelles, IMB, Université de Bordeaux Lénaïc Chizat, CEREMADE, Université Paris Dauphine Emilie Chouzenoux, Laboratoire Gaspard Monge, Univ. Paris-Est Quentin Denoyelle, CEREMADE, Université Paris Dauphine Alexandre Gramfort, Telecom ParisTech Franck Iutzeler, LJK, Université Grenoble Alpes Bruno Lévy, ALICE, Inria Nancy Grand-Est / Loria Klas Modin, Chalmers Univ. of Technology Clarice Poon, DAMTP, University of Cambridge Carola-Bibiane Schönlieb, DAMTP, University of Cambridge Pauline Tan, DOTA, ONERA Benedikt Wirth, Institute for Computational and Applied Mathematics, Universität Münster

    While optimization has been playing a key role in signal and image processing for many decades, the signal and image processing community has made tremendous progress in the last ten years by adopting proximal methods. That breakthrough paved the way to large scale image processing in inverse problems (compressive sensing, deconvolution, inpainting.) as well as new and more involved modalities.
    In the meantime, optimal transport has evolved from a purely theoretical field into a new and exciting challenge for numericians, raising specific issues in terms of optimization and numerical analysis (entropic regularization, degenerate elliptic schemes.).
    The goal of this workshop is to bring together confirmed and upcoming experts in both fields, exchanging on their most recent advances and problems.


    Charles-Alban Deledalle (IMB) Charles Dossal (IMB) Vincent Duval (INRIA) Nicolas Papadakis (IMB) Julien Rabin (GREYC) François-Xavier Vialard (CEREMADE/INRIA)

  • April 17-21, 2017 - Amirkabir university of Technology - Tehran - Iran



    Preleminary organization announcement

    Organization of the second edition of the workshop BigDataLifeScience has started.

    The preliminary program is:

    two days of scientific work (April 17-18, 2017) one day workshops (April 19, 2017) for CNRS-AUT common projects definition one day workshop and visit to Shiraz (April 20) and one day workshop and visit to Ispahan (April 21)

    A long term objective that we are focusing is the creation of common
    working projects and perhaps a common CNRS-AUT research center (LIA).

    Conference general chair: Ali Mohammad-Djafari
    Laboratoire des signaux et systèmes (L2S), CNRS-CentraleSupélec-Univ. Paris Saclay, Gif-sur-Yvette, France Executive chair: Mina Aminghafari
    Faculty of Mathematics and Computer Science of AUT Local organisers:
    Adel Mohammadpour, Mathematics Dept. AUT
    Mina Aminghafari, Mathematics Dept. AUT

  • Centre International de Rencontres Mathématiques (CIRM) - Marseille/Luminy - 15 - 19 may 2017


    19ème conférence en Géométrie stochastique, stéréologie et analyse d'images
    Centre International de Rencontres Mathématiques (CIRM), à Marseille/Luminy du 15 au 19 mai 2017.


    Cette conférence est la 19ème d'une série de workshops intitulés Stochastic Geometry, Stereology and Image Analysis (SGSIA) qui ont été organisés tous les deux ans depuis 1981. L'événement qui a lieu pour la première fois en France constitue la principale occasion de promouvoir les avancées récentes en géométrie stochastique, statistique spatiale, géométrie convexe et intégrale, stéréologie et analyse d'image. Il est simultanément le rassemblement annuel de tous les membres du groupement de recherche Géométrie Stochastique (GeoSto, GDR 3477) qui est financé par le CNRS depuis 2012. Le programme scientifique devrait couvrir tous les aspects des thèmes principaux tout en s'aventurant occasionnellement un peu au-delà de leurs frontières. L'emploi du temps permettra naturellement les discussions et interactions entre les participants.

    Liens vers les précédents workshops SGSIA:

    Liens vers les précédentes conférences GeoSto:

    Comité scientifique

    François Baccelli (INRIA Paris et University of Austin) Wilfrid Kendall (University of Warwick) Marie-Colette van Lieshout (CWI Amsterdam) Claudia Redenbach (Kaiserlautern Universität) Joseph E. Yukich (Lehigh University)

    Comité d'organisation

    Pierre Calka (Université de Rouen) Jean-François Coeurjolly (Université du Québec à Montréal) David Coupier (Université Lille 1) Anne Estrade (Université Paris Descartes) Ilya Molchanov (University of Bern)

    Conférenciers invités

    Adrian Baddeley (University of Western Australia) Antonio Cuevas (Universidad Autonoma de Madrid) Dominique Jeulin (Mines ParisTech) Rolf Schneider (Universitat Freiburg) Gunter Last (Karlsruhe Institute of Technology) Jean-Michel Morel (ENS Cachan) Giovanni Peccati (Luxembourg University) Perla Sousi (University of Cambridge) Martina Zahle (Jena Universitat) Johanna Ziegel (University of Bern)

  • Friday 2.PM weekly seminar - Room 1016 - Bâtiment Sophie Germain, University Paris-7 - France


    Page Web officielle

    Séminaire de géométrie et physique mathématique

    organisé par Serguei Barannikov, Daniel Bennequin, Christian Brouder,
    Frédéric Hélein et Volodya Roubtsov

    Bâtiment Sophie Germain, Paris 13ème
    (voir le plan d'accès) Salle 1016
    Année précédente (2015-2016)

    Prochaine scéance Année 2015-2016:

    Vendredi 28 octobre 2016, 14 h
    salle 1016 :
    Charles-Michel Marle,

    Les travaux de Jean-Marie Souriau en mécanique statistique et en thermodynamique, et en particulier sa généralisation de la notion d'état de Gibbs aux actions hamiltoniennes d'un groupe de Lie
    Résumé : Après un bref rappel des principes de la mécanique statistique classique et de la notion d'état de Gibbs, et la présentation de quelques résultats qui s'en déduisent (équation d'état d'un gaz parfait monoatomique, lois de distribution de l'impulsion de Maxwell-Boltzmann pour un gaz non relativiste et de Maxwell-Jüttner pour un gaz relativiste, loi de Dulong et Petit pour la chaleur spécifique des solides), je présenterai la généralisation de la notion d'état de Gibbs aux actions hamiltoniennes d'un groupe de Lie sur une variété symplectique, due à Jean-Marie Souriau. La notion d'état de Gibbs usuelle apparaît comme un cas particulier dans lequel le groupe de Lie, de dimension 1, est le groupe des translations temporelles. Mon exposé sera une préparation à celui de Frédéric Barbaresco qui aura lieu le 25 novembre 2016.

    Vendredi 4 novembre 2016, 14 h
    salle 1016 :
    Juan Pablo Vigneaux

    Cohomologie de l'information
    Résumé : Baudot et Bennequin [1] ont introduit une cohomologie adaptée à la théorie de l'information. Cette cohomologie suit les constructions générales décrits dans le SGA IV (théorie des topos) ; le topos de l'information est le topos de préfaisceaux sur un site définie par des variables aléatoires.

    On peut définir une famille des faisceaux (F_q) (pour (q>0)), tels que l'entropie de Shannon génère le groupe (H^1(F_1)) et les entropies de Tsallis génèrent (H^1(F_q)), pour (q) différent de 1. Autres fonctions d'information apparaissent aussi comme cocycles et la théorie s'étend au cas quantique.

    L'axiomatisation usuelle de l'entropie, due à Shannon, peut être interprété dans le cadre d'extensions (des faisceaux) d'algèbres et correspond au cas scindée. Cela suggère des interprétations possibles pour les classes d'ordre supérieur.

    [1] Baudot, P.; Bennequin, D. The Homological Nature of Entropy. Entropy 2015, 17, 3253-3318.

    à suivre ...

    Vendredi 18 novembre 2016, 14 h

    Pas de séminaire

    Vendredi 25 novembre 2016, 14 h
    salle 1016 :
    Frédéric Barbaresco
    Groupe Thalès

    Modèle de la "Thermodynamique des groupes de Lie" de Jean-Marie Souriau: cohomologie symplectique de l'Information et métrique de Fisher-Souriau
    Résumé : résumé détaillé
    Voir aussi la prépublication : Geometric Theory of Heat from Souriau Lie Groups Thermodynamics and Koszul Hessian Geometry: Applications in Information Geometry for Exponential Families et les références dans le numéro spécial de Differential Geometrical Theory of Statistics, notament l'article de Charles-Michel Marles

    Vendredi 8 décembre 2016, 14 h
    salle 1016 :
    Tilmann Wurzbacher
    Université de Lorraine, Metz

    Applications moment en géométrie multisymplectique
    Résumé :

    Séances précédentes :

    Vendredi 14 octobre 2016, 14 h
    salle 1016 :
    Penka Vasileva Giorgieva

    Théorie de Gromov-Witten réelle
    Résumé :

    Vendredi 7 octobre 2016, 14 h
    salle 1016 :
    Vincent Caudrelier
    Université de Leeds (Royaume-Uni)

    Lagrangian and Hamiltonian structures in an integrable hierarchy
    Résumé : The classical and quantum versions of the R matrix are the cornerstones in classical and quantum integrable systems, typically formulated in 1+1 dimensions. They are the heart of the theory developed by the Fields medallist V. Drinfeld. However, they traditionally concentrate all the attention on only one of the independent variables: the space one while time evolution is encoded more or less trivially. The latter point is in fact deeply related to the boundary conditions imposed on the system. A big success of the theory of classical integrable systems is the systematic Hamiltonian formulation of the corresponding PDEs. The essential object capturing the Hamiltonian properties (infinite number of conserved quantities, etc) is the so-called classical r-matrix. Motivated originally by the question of integrability of certain field theories in the presence of defects, we will show how a dual Hamiltonian structure naturally emerges which gives a fully fledged r-matrix structure to the time variable. This is inspired and related to the notion of covariant field theory. The interplay between the standard classical r-matrix structure and the dual one raises many questions and begs for a "multisymplectic r-matrix theory". Time permitting, we will speculate on other related open questions: quantization and out-of-equilibrium systems.

  • Conference for Shannon 100th birthday - IHP - 26- 28 october 2016 - Paris - France




    Vendredi 28 octobre 2016

    Frank Nielsen (École Polytechnique)
    The dual geometry of Shannon information and its applications
    Abstract: In information geometry, the negative Shannon entropy, called the Shannon information, is a strictly convex and differentiable function that induces a dually flat manifold structure equipped with the Kullback- Leibler divergence. In this talk, I review the concept of dual geometries, introduce the dual space of spheres, and describe the role of divergences in information theory, statistics, pattern recognition and machine learning.
    Frank Nielsen received his PhD (1996) and his habilitation (2006) on computational geometry from the University of Nice-Sophia Antipolis, France. After the French national service, he joined Sony CSL (Japan) in 1997. He is currently professor in the computer science department of École Polytechnique (France). He co- organizes with Frédéric Barbaresco (Thales) the biannual Geometric Sciences of Information (GSI) conference, and is an associate editor of the Springer Journal of Mathematical Imaging and Vision and of MDPI Entropy.

    Ruediger Urbank (EPFL)
    Happy Numbers: 68 Years of Coding, 6² + 8² = 100 Years of Shannon, 1² + 0² + 0² = 1 Goal
    Abstract: This year, we celebrate Shannon’s 100th birthday and it has been 68 years since he laid the foundations of communications. To realize his number 1 goal or error free communication we use error correcting codes. Every time we make a call, connect to WiFi, download a movie, or store a file, they help us get things right. The journey began with codes based on algebraic structures such as Reed-Muller and Reed- Solomon codes. Then lattices helped convey continuous-valued signals. Slowly, deterministic codes made way for random sparse graphs codes with low-complexity message-passing decoding, such as Turbo codes and LDPC codes. The new millennium brought us Polar codes that use the chain rule of mutual information to achieve capacity and spatially-coupled codes that exploit the physical mechanism that makes crystals grow to simultaneously achieve the capacity of a large family of communication channels. Recently, the story has come full circle, and the symmetry inherent in algebraic constructions has brought the focus back on Reed-Muller codes. I will describe how ideas from such diverse areas as abstract algebra, number theory, probability, information theory, and physics slowly made it from the blackboard into products, and outline the main challenges that we face today.
    Ruediger Urbanke (Phd, WashU, St. Louis, 1995) has been obsessed with questions in coding theory for the past 20 years. Fortunately his progress has been slow so that there are many problems left for him for the next 20 years. He likes sabbaticals and owns more bicycles than can be rationally justified. Before joining EPFL in 1999, he enjoyed working at Bell Labs (Murray Hill) at the Mathematics of Communications Group.

    Robert G. GALLAGER (MIT)
    Claude Shannon: His life, modus operandi, and impact

    Cédric Villani (Directeur de l’Institut Henri Poincaré)
    Discours de cloture


  • December 7th 2016 - IRISA INRIA Rennes France




    Distance Geometry Day in Rennes
    December 7th, 2016

    Distance Geometry (DG) is a consolidated research area, where mathematics and computer science are at its foundation. Classical applications of DG include the one of identifying the 3D conformations of biological molecules, the problem of localizing sensors in a given network, and the clock synchronization problem. Morever, recent research activities have been showing that there are several other applications that can be actually faced by DG. Examples are human motion adaptation, crowd simulations and virtual camera control.

    This DG Day (DGD) has as main purpose to bring together researchers working on different aspects of the DG and on some of its applications. The talks will give either an overview of the current research on DG solution methods, or describe particular applications where the DG approach can bring to the discovery of new interesting scientific results.

    DGD16 date and venue

    The DGD16 will be held on December 7th, 2016, at IRISA, INRIA Rennes, in Les Minquiers room.

    Invitation-based scientific program

    9:30 - 9:50
    Registration and Coffee

    9:50 - 10:00
    Opening, DGD16 Chair.

    10:00 - 10:50
    Mathematical Gems in Distance Geometry
    Leo Liberti, CNRS-LIX, École Polytechnique, Palaiseau
    (joint work with: C. Lavor)
    Distance geometry focuses on the concept of distance rather than points and lines. Its fundamental problem asks to draw a weighted graph in a given K-dimensional Euclidean space, so that each edge is drawn as a segment with length equal to the weight, and it has applications to many fields of science and engineering (e.g. protein folding, wireless networks, robotic control, nanostructures and more). Distance geometry results are scattered throughout the whole history of mathematics starting with the Greeks. I will present some of those I find most beautiful, from a selection including: Heron's theorem, Cauchy's theorem about rigidity of convex polyhedra, Goedel's theorem about realizability on a sphere, Schoenberg's theorem linking Euclidean Distance Matrices and Positive Semidefinite Matrices, and a surprising theorem of Johnson and Lindenstrauss about approximately projecting realizations from very high dimensional spaces.

    10:50 - 11:20
    Practical Implementation Considerations of Interval Branch-and-Prune for Protein Structure Determination
    Bradley Worley, Institut Pasteur, Paris
    (joint work with: T. Malliavin, B. Bardiaux, M. Nilges, C. Lavor, L. Liberti)
    The interval Branch and Prune (iBP) algorithm for obtaining solutions to the interval Discretizable Molecular Distance Geometry Problem (iDMDGP) has proven itself as a powerful method for molecular structure determination. However, substantial obstacles still must be overcome before iBP may be employed as a tractable general-purpose alternative to exist- ing structure determination algorithms. This work demonstrates how careful choice of data structures and mathematical frameworks leads to dramatic improvements in the performance of iBP in the specific case of protein structure determination. Moreover, it demonstrates how "soft" pruning of protein conformational space using interval-derived pseudo-potential energy functions may be utilized in lieu of "hard" pruning, which can become intolerant to otherwise acceptable minor geometric inconsistencies in molecular structures.

    11:20 - 11:50
    Feasibility Check for the Distance Geometry Problem: an Application to Molecular Conformations
    Rosa Figueiredo, LIA, University of Avignon
    (joint work with: A. Agra, C. Lavor, N. Maculan, A. Pereira, C. Requejo)
    The distance geometry problem (DGP) consists in finding an embedding in a metric space of a given weighted undirected graph such that for each edge in the graph, the corresponding distance in the embedding belongs to a given distance interval. We discuss the relationship between the existence of a graph embedding in a Euclidean space and the existence of a graph embedding in a lattice. Different approaches, including two integer programming (IP) models and a constraint programming (CP) approach, are presented to test the feasibility of the DGP. The two IP models are improved with the inclusion of valid inequalities, and the CP approach is improved using an algorithm to perform a domain reduction. The main motivation for this work is to derive new pruning devices within branch-and-prune algorithms for instances occurring in real applications related to determination of molecular conformations, which is a particular case of the DGP. A computational study based on a set of small-sized instances from molecular conformations is reported. This study compares the running times of the different approaches to check feasibility.

    11:50 - 14:00
    Lunch break, light meal offered to invited speakers and scientific committee.

    14:00 - 14:50
    The Interplay between Graph Rigidity and Multi-Robot Coordination
    Paolo Robuffo Giordano, INRIA Rennes
    Graph theory has a predominant role in the field of multi-robot coordination problems (spanning, e.g., distributed formation control and estimation schemes). Indeed, graphs are a convenient (combinatorial) abstraction for representing the various sensing/communication constraints among pairs of robots in the environments. Vertexes represent robots, and edges the possibility for a robot to measure/communicate with another robot in the group.
    In this context, the notion of graph rigidity is gaining popularity since rigidity of a multi-robot "framework" (i.e., formation) has proven to be a necessary condition for the solution of many formation control/cooperative localization problems. Moreover, exploiting the tools of algebraic graph theory, one can associate combinatorial graph properties, such as rigidity, to some suitable spectral properties (i.e., eigenvalues) of associated matrixes. This spectral interpretation of graph-related properties makes it possible to ultimately design simple "gradient-like" controllers for the robot group able to solve formation control and localization problems in a decentralized way.
    This talk will review the concepts of graph rigidity in the context of multi-robot applications, and present some recent results obtained with a team of quadrotor UAVs (drones) used as robotics platforms.

    14:50 - 15:20
    Regulating Distance in Human Movement Coordination
    Laurentius Meerhoff, IRISA Rennes
    (joint work with: J. Pettré, R. Kulpa, A. Crétual, S. Lynch, A-H. Olivier)
    The relationship between an agent (i.e., an independently decision-making entity) and its environment is the central tenet of an ecological approach to human movement. Displacement, and subsequently distance regulation, is one of the fundamental aspects of human movement. Distance needs to be regulated to avoid collision (e.g., in traffic), find a route in a complex environment (e.g., in crowd navigation) or perform coordinated behavior (e.g., in sports). In such social settings, perhaps the most prominent aspect of the environment are the other agents. Therefore, we study how collision avoidance is achieved when multiple walkers cross their trajectories. Previously it was shown that the locomotor trajectories in pairwise interactions emerge through an avoidance of risk of collision. The reciprocal interactions between these agents make the behavior complex. Moreover, many common daily life interactions encompass more than two agents, exponentially increasing the complexity. Based on animal models, it has been argued that coordination results from micro-level interactions based on simple behavioral rules which in turn lead to complex macro patterns (cf., flocking birds, schooling fish or marching on a suspension bridge). For human-to-human interactions, coordination additionally incorporates a social aspect depending on the specific situation (e.g., attractiveness of another agent). Currently, we are researching how inter-agent coordination emerges when multiple (n > 2) agents are interacting. Each agent's contribution to the inter-agent distance metrics are used to characterize the interactions. This work has implications for understanding how coordination emerges in multi-agent systems as for example crowds of people or sport teams.

    15:20 - 15:50
    Coffee break

    15:50 - 16:30
    Title to be communicated
    Marc Christie, IRISA, University of Rennes 1
    Abstract to be communicated.

    16:30 - 17:00
    Interaction-based Human Motion Analysis
    Yijun Shen, Northumbria University, Newcastle
    (joint work with: H.P.H. Shum, E.S.L. Ho)
    Traditional methods for motion analysis consider features from individual characters. However, the contextual meaning of many motions is defined by the interaction between characters. There is little success in adapting interaction-based features in evaluating interaction difference, as they are either topologically different across interactions or high dimensional. In our work, we propose a new unified framework for motion retrieval and analysis from the interaction point of view. We adapt the Earth Mover’s Distance to optimally match interaction features of different topology, which allows us to compare different classes of interactions and discover their intrinsic semantic similarity.
    We construct a comprehensive kick-boxing interaction database that is open for public for research benchmark. Experimental results show that our method outperforms existing research and aligns better with human perceived interaction similarity.

    Post DGD16 publications

    A special issue of Optimization Letters (OPTL, Springer) will collect short papers on the topics related to the DGD16. The special issue will be co-edited by the co-presidents of DGD16 committee. The call for papers can be downloaded here. All accepted papers will be published online individually, before print publication. The editorial system will be ready soon to accept submissions.

    Previous DG events

    DIMACS Workshop on Distance Geometry: Theory and Applications (DGTA16)
    Rutgers University, New Jersey, USA. F. Alizadeh and L. Liberti co-Chairs. August 2016.
    Many Faces of Distances (MFD14)
    Campinas, São Paulo, Brazil. C. Lavor and M. Ferer co-Chairs. October 2014.
    Workshop on Distance Geometry and Applications (DGA13)
    Manaus, Amazonas, Brazil. N. Maculan General Chair. June 2013.

    DGD16 Chair

    Antonio Mucherino, IRISA/INRIA and University of Rennes 1.

    DGD16 Presidents of Scientific Committee

    Antonio Mucherino, IRISA/INRIA and University of Rennes 1.
    Carlile Lavor, IMECC-UNICAMP, Campinas, São Paulo.

    Scientific Committee

    Ludovic Hoyet, INRIA Rennes Davide Frey, INRIA Rennes Sylvain Collange, INRIA Rennes Jérémy Omer, INSA, Rennes Rosa Figueiredo, University of Avignon Thérèse Malliavin, Institut Pasteur, Paris Franck Multon, University of Rennes 2

    Local organization

    Nathalie Denis, INRIA Antonio Mucherino, IRISA/INRIA and University of Rennes 1

    A special thanks

    To Mila Nobis for having designed our logo!


    The DGD16 is partially supported by CNRS (INS2I PEPS projects 2016), IRISA and INRIA Rennes.

  • Geo-Sci-Info



    Ces journées seront rythmées en deux temps:

    3 mini cours de 2h30
    Information Based Complexity, par Henryk Wozniakowski (Columbia University et Warsaw University)
    Compressive Sensing, par Holger Rauhut (RWTH Aachen University)
    QuasiMonteCarlo Methods, par Dirk Nuyens (KU Leuven) 7 exposés de 40 mn: confirmés
    Albert Cohen (Université Pierre-et-Marie Curie)
    Emmanuel Gobet (Ecole Polytechnique)
    Alexey Khartov (St-Petersburg State University)
    Peter Mathé (Weierstrass Institute, Berlin)
    Giovanni Migliorati (Université Pierre-et-Marie Curie)
    Erich Novak ((Universität Jena)

    bandau sponsor.jpg

  • Geo-Sci-Info


    Colloque International de Théories Variationnelles (CITV) 2017
    SOURIAU COLLOQUIUM - Amboise du 25/06 au 30/06/2017


    Jean-Marie Souriau ( and, born on 3 June 1922 and dead on 15 March 2012, was a French mathematician, known for works in symplectic geometry, in which he was one of the pioneers.

    He has left us not solely a master piece of scientific work through his treatises on dynamical systems, relativity and quantum mechanics but Jean-Marie Souriau –the professor and the human person– has also given us a sound philosophical vision of the world through his last book, “Grammaire de la nature”.

    Originally founded in 1956 by Jean-Marie Souriau, his doctoral students and friends, re-launched by Claude Vallée in 1996, the Colloquium entitled “Colloque International de Théories Variationnelles” (CITV) provides today an informal setting to present and discuss the state-of-the-art and the most recent findings concerning Mathematics, Physics, Mechanics and their interactions.

    Every year during a week, the CITV brings together a small group of participants hoping to work within the founder’s spirit. In marked contrast to standard Colloquia, the CITV’s style is completely informal:

    The schedule is prepared day-to-day and, if necessary, the talk durations may be adjusted under way.

    Preference is given to present ideas on the blackboard even if modern video presentations are also allowed.

    Emphasis is put on scientific maturity and creative ideas as opposed to technicalities.

    A large time is devoted to questions, discussion and exchange of ideas.

    The spectrum of topics being very broad, a special attention is paid to present advanced concepts in simple terms for people with strong scientific background but the non-specialists of the topic.

    The oral transmission of the knowledge is preferred –but not opposed– to modern communication based on paper publication.

    Epistemological talks are sometimes given in the evening and also open to accompanying persons.

    The social program is not reserved for accompanying persons but convivial interludes are scheduled in the afternoon for scientific participants sharing cultural visits and excursions with them.

    It my pleasure to announce that the 61th SOURIAU COLLOQUIUM will be held on June 25-30, 2017 at Amboise (Indre-et-Loire department, France).

    Amboise is a small and pleasant city at 2 hours from Paris by TGV and in the heart of the region of the castles of the Loire. With its castle and above all the manor of Clos-Lucé where Leonardo da Vinci spent his last days, Amboise is a cheerful reminder of what the Italian Renaissance brought to the area five centuries ago (

  • Institut Henri Poincaré, Paris, France September 4th - December 15th 2017


    General information

    Analysis in Quantum Information Theory
    Quantum Information Theory (QIT) is a rapidly developing field whose significance ranges from fundamental issues in the foundations of quantum theory to new state-of-the-art methods for secure transmission of information. The potential for powerful new methods of computation, data transmission and encryption has led to new perspectives on such entire fields as computational complexity and Shannon information theory. Work in this highly interdisciplinary and competitive area overlaps many different fields of mathematics and has widespread applications in fields like computer science and physics. The main feature of this program will be a systematic exploration of QIT via analysis (considered in a broad sense). More precisely, we will concentrate on the role of operator structures and of probabilistic tools in QIT. The operator structures of importance in QIT are in particular operator algebras, operator spaces, and operator systems. Conversely, the impact of quantum information science on these fields has been significant in the last few years. Operator algebras have a long history as a framework for quantum theory. In QIT, interactions with the environment play a major role, corresponding to the auxiliary spaces which are an essential component of operator spaces and systems. The probabilistic tools include concentration of measure, random matrix theory and large deviation theory. A related area which has probabilistic flavor, but deserves to be mentioned separately, is the asymptotic geometry of high dimensional convex bodies, which grew out of geometric functional analysis and classical convexity. At the intersection of operator algebras and (quantum) probability, there is also free probability theory, which was developed by Voiculescu in the 1990s with the aim of classifying II1 factors in von Neumann algebra theory. Free probability also turns out to play a major role in QIT, a fact which will be emphasized during the program.

    How to participate
    If you are interested in participating, you must register on the IHP website; we strongly encourage you to do so at your earliest convenience. Deadline for financial support applications: 15/03/2017.
    Structure of the semester
    On "normal" weeks, the scientific programme will be limited to 0-2 seminars per day in order to give participants time for discussions and collaborative work. Several series of educational lectures targetted at young researchers will also be organized.

    On "exceptional" weeks, some events will be organized

    04.09.17 - 08.09.17 - Summer School "Mathematical Aspects of Quantum Information"
    11.09.17 - 15.09.17 - Workshop "Operator algebras and Quantum Information Theory"
    23.10.17 - 28.10.17 - Workshop "Probabilistic techniques and Quantum Information Theory"
    11.12.17 - 15.12.17 - Conference "Quantum Information Theory"


    All the events (except the summer school) will take place at the Institut Henri Poincaré, located in downtown Paris.


    Guillaume Aubrun Benoît Collins Ion Nechita Stanislaw Szarek

    Scientific Commitee

    Patrick Hayden
    Marius Junge
    Iordanis Kerenidis
    Vern Paulsen
    Gilles Pisier
    Mary Beth Ruskai
    Andreas Winter
    Quanhua Xu

  • Geo-Sci-Info


    All the abstracts of the sessions are now available online in the Entropy 2018 website


    Session Start:
    Monday 14 May 2018: 08:00 - 12:30 / 14:00 - 18:00
    Tuesday 15 May 2018: 08:30 - 12:30 / 14:00 - 18:00 / Conference Dinner: 20:30
    Wednesday 16 May 2018: 08:30 - 12:30 / 14:00 - 18:00

  • Geo-Sci-Info

    The Ninth International Conference on Guided Self-Organisation (GSO-2018) : Information Geometry and Statistical Physics

    March 26 - 28, 2018
    Max Planck Institute for Mathematics in the Sciences

    The goal of Guided Self-Organization (GSO) is to leverage the strengths of self-organization (i.e., its simplicity, parallelization, adaptability, robustness, scalability) while still being able to direct the outcome of the self-organizing process. GSO typically has the following features:

    (i) An increase in organization (i.e., structure and/or functionality) over time;

    (ii) Local interactions that are not explicitly guided by any external agent;

    (iii) Task-independent objectives that are combined with task-dependent constraints.

    GSO-2018 is the 9th conference in a bi-annual series on GSO. Recent research is starting to indicate that information geometry, nonequilibrium statistical physics in general, and the thermodynamics of computation in particular, all play a key role in GSO. Accordingly, a particular focus of this conference will be the interplay of those three topics as revealed by their relationship with GSO.

    The following specific topics are of special interest:

    information-driven self-organisation complex systems and networks non-equilibrium statistical physics non-extensive statistical mechanics physics of information and computation information dynamics generalised entropies generalised relative entropies alpha geometry and alpha statistics constraints and maximum entropy principle information-geometric aspects of Fokker-Planck and Kolmogorov equations

    More Information

    Conference Homepage GSO Workshop Series Keynote Speakers Tentative Program Call for Abstracts Registration

    Date and Location

    March 26 - 28, 2018
    Max Planck Institute for Mathematics in the Sciences
    Inselstraße 22
    04103 Leipzig
    see travel instructions

    Organizing Committee

    Nihat Ay MPI for Mathematics in the Sciences Leipzig (Germany) Mikhail Prokopenko University of Sydney (Australia)

    Program Committee

    Nihat Ay, MPI for Mathematics in the Sciences, Leipzig (Germany) Domenico Felice, Università degli Studi di Camerino, Camerino (Italy) Carlos Gershenson, Universidad Nacional Autónoma de México, Computer Sciences Department, Mexico City (Mexico) Paolo Gibilisco, Università degli Studi di Roma "Tor Vergata", Facoltà di Economia, Roma (Italy) Daniel Polani, University of Hertfordshire, Department of Computer Science, Hatfield (United Kingdom) Mikhail Prokopenko, University of Sydney, Sydney (Australia) Richard Spinney, University of Sydney, Sydney (Australia) Justin Werfel, Harvard University, Cambridge (USA) Larry Yaeger, Google Inc., San Francisco (USA) G. Çiğdem Yalçın, İstanbul Üniversitesi, İstanbul (Turkey)

    Administrative Contact
    Antje Vandenberg
    MPI for Mathematics in the Sciences

  • Geo-Sci-Info



    Time Schedule & Deadlines:

    Abstract Submission: 6 Oct. 2017 Acceptance Notification: 20 Oct. 2017 Full Paper Submission: 3 Nov. 2017

    Welcome from the Chair of the 4th ECEA

    You are cordially invited to participate in the 4th International Electronic Conference on Entropy and Its Applications. Building on the success of the past three events in this sequence, the conference is designed to bring together researchers working in the field, to present and discuss their recent contributions, without needing to leave the comfort of their home.

    Conserved quantities such as energy, and monotonic quantities such as entropy, are fundamental to our understanding of high-dimensional dynamical systems. Since the foundations of irreversibility were laid in 19th century thermodynamics, and the conceptual analogy between statistical mechanics and information theory (both classical and quantum) was made in the 20th century, inter-related concepts of entropy have been fruitfully applied to a large and expanding list of fields, including economics, biology, computer science, operations research using maximum entropy estimates, linguistics and the social sciences.

    The conference will be organized into six sessions, which reflect the inter-disciplinary nature of entropy and its applications:

    Section A
    Statistical Physics: Statistical Mechanics, Irreversibility, Fluctuation Theorems, Equilibrium and Non-Equilibrium Distributions, Phase Transitions, Stochastic Systems, Chaos and Nonlinear Dynamics, Population Dynamics and Genetics Section B
    Information and Complexity: Simplicity and Complexity, Shannon Entropy, Kullback–Leibler Divergence, Channel Capacity, Alternative Entropies, Coding, Operations Research, Forecasting, Symmetry Breaking, Similarity Section C
    Thermodynamics in Materials: Chemical thermodynamics, the Second Law, Free Energy, Enthalpy, Self-Organization, Biochemical Networks, Nano-Scale Physics, Molecular Theory of Fluids Section D
    Quantum Information and Foundations: Quantum Foundations, Quantum Probability and non-Kolmogorov Models, Quantum-Like Models in Cognition, Decision Making, Psychology, Social and Political Science, Economics and Finances, Bell's Inequality, Quantum Nonlocality, Contextuality, Pure and Mixed States, Superposition, De-coherence, Quantum Computing, Born Rule and Generalizations, Causality and Randomness, Role of Conscious Observer Section E
    Machine Learning: Artificial Intelligence, Neural Networks, Cybernetics, Robotics, Man–Machine Interfaces, Bio-mimic Algorithms Section F
    Astrophysics and Cosmology: Evolution of Stars, Gravitating Systems, Black Holes, The Universe Section P
    Posters: Posters can be presented stand-alone, i.e., without an accompanying proceedings paper or conference presentation. Posters will be available online on this website during and after the e-conference. However, posters will not be added to the proceedings of the conference.

    Accepted papers will be published in the proceedings of the conference, and selected/extended papers will be considered for publication in Entropy with a 20% discount off the APC. Entropy is an open access publication journal of MDPI in the field of entropy and information theory.

    Conference Chair

    Prof. Dr. Philip Broadbridge

    Editorial Board of the Journal Entropy
    Department of Mathematics and Statistics
    La Trobe University, Melbourne

    Section Chairs

    Dr. Antonio M. Scarfone (Politecnico di Torino, Torino, Italy) Prof. Dr. Miguel Rubi (Universitat de Barcelona, Barcelona, Spain) Prof. Dr. Raúl Alcaraz Martínez (University of Castilla-La Mancha, Cuenca, Spain) Dr. Dawn E. Holmes (University of California, Oakland, CA, USA) Prof. Dr. Alexander Gorban (University of Leicester, Leicester, UK) Dr. Andriy Olenko (La Trobe University, Melbourne, Australia) Prof. Dr. Leslie Glasser (Curtin University, Perth, Australia) Prof. Giacomo Mauro D'Ariano (University of Pavia, Pavia, Italy) Prof. Dr. Andrei Khrennikov (Linnaeus University, Växjö, Sweden) Dr. Michael J. Way (NASA Goddard Institute for Space Studies, New York, NY, USA)

    Scientific Advisory Committee Members

    Prof. Anne Humeau-Heurtier (University of Angers, Angers cedex, France) Dr. Takuya Yamano (Kanagawa University, Kanagawa, Japan) Prof. Dr. Carlo Cattani (Engineering School (DEIM) University of Tuscia, Viterbo, Italy) Dr. Renaldas Renaldas Urniezius (Kaunas University of Technology, Lithuania) Dr. Robert Niven (The University of New South Wales at ADFA, Canberra, Australia)

    Entropy OA-01.png

  • Geo-Sci-Info

    Entropy Young Investigator Award 2018

    The award will consist of:

    2500 Swiss Frances; the book Probability Theory: The Logic of Science by Edwin T. Jaynes; a commemorative plaque; a waiver of registration fees and a talk for the conference “Entropy
    2018: From Physics to Information Sciences and Geometry


    The nominee has contributed outstanding research in the fields
    covered by the Entropy journal, see details at The nominee should have received his/her PhD within the last eight
    years (by 31 December 2017), and not yet hold a permanent professorship. The nominee should be 40 years of age or under (by 31 December 2017).

    Required application documents:

    CV (including date on which the PhD degree was awarded and a list of
    publications) Research description (up to two pages) Nomination letter from his/her supervisor, research director or
    department head

    Please submit your application online at
    by 31 December 2017. The winner will be announced by the end of
    January 2018.

  • Participate: following Azimuth, bring Climate, Environmental, health, and cultural data to Unesco World Heritage.



    This page and project is under-construction and is just a preliminary draft.


    Following the last US elections, safety of US government climate data appeared at risk (Brady Dennis, Scientists are frantically copying U.S. climate data, fearing it might vanish under Trump, Washington Post, 13th December 2016). The new head the Environmental Protection Agency nominated by the government, Scott Pruitt, called himself a "leading advocate against the EPA's activist agenda". The new head of the Department of Energy, Rick Perry, claimed that "we have been experiencing a cooling trend", and said "there are a substantial number of scientists who have manipulated data so that they will have dollars rolling into their projects".
    Some US researchers have reported deletion of environmental data archivs (ex: V.Hermann report in the gardian 28th march 2017). An abreviated timeline of the Trump administration’s environmental actions and policy changes, as well as reactions to them, is maintained and periodically updated by the National Geographic (National Geographic- A Running List of How Trump Is Changing the Environment). On the the 1st of July 2017, president Donald Trump announced that U.S. is pulling out of Paris Climate Agreement (a review is available on wikipedia).

    In reaction, several initiatives to back up the many climate databases held by US government agencies arose in order to prevent their potential removing by the administration. Jan Galkowski, a statistician working at Akamai Technologies, began downloading climate data on the 11th of December 2016. John Baez, a mathematician who animates the Azymuth project, joined in to coordinate publicity for the project and it gave birth to the Azimuth Climate Data Backup Project. Azimuth initiated a preliminary crowd founding and achieved some backup of some important publicly open climate data basis. Scott Maxwell (Google) set up a 10-terabyte account on Google Drive and started backing up data. Sakari Maaranen (Ubisecure) and Hetzner set up a server with that provides 10 Tb of storage, gigabit bandwidth and 30 Tb of a monthly traffic. A crowd funding campaign was initiated and 40 terabytes of US government databases on climate change and the environment were backed up on servers.
    In a joint and parallel effort, the Data Refuge Project of the University of Pennsylvania organised the climate mirror project, an open project to mirror public climate datasets. Soon after some datasets were published, individuals started mirroring them. was one of the first to help and currently a lot of datasets are hosted here.
    Pierre Collet, Anne Jeannin-Girardon, Pierre Baudot (Complex System-Digital Campus Unitwin Unesco and the University of Strasbourg) set up a server with 40Tb for the Azimuth Climate Data Backup Project. The list of databasis and progress of the backup by the Azimuth Climate Data Backup Project is available in this archiv. The safety of US government environmental databases safely back is ensured by computing hash codes for these datasets to help to prove the backups are authentic. More informations are available on Azimuth Climate Data Backup Project.

    All this has been achieved thanks to personal and volunteers initiatives and funding, and it is now time for an international institution, Unesco to get involved in order to allow a long term backing up and to avoid some other future problem of this kind. These local US and recent events points out a general problem that should have been assessed long ago, upon the existence and status of archivs of data that appear crucial in sustainable development at the global scale and on the long term. Such data shall not be vulnerable to some ponctual or local political fluctuations. Since some other data, like health data (ex: epidemiological data), ecological data (ex: biodiversity survey) or cultural (indigenous oral and writing cultural data), in the field of Unesco, are faced to the same problem of potential deletion, the scope of the climate data archivs started by the US recent events shall be broaden and pursued on the long run and it is indeed in the original foundational guidelines of Unesco to enlarge the data archivs to biodiversity and cultural data.

    Idea an aims: A status of Unesco World Heritage for Climate, Heath and Cultural critical data

    “What steam was to the 18th century, electricity to the 19th, and hydrocarbons to the 20th, data will be to the 21st century. That’s why I call data a new natural resource.” Ginni Rometty, Chairman, President and CEO of IBM. Indeed some data are peculiarly sensible and critical environmental, cultural, societal and scientific ressources that has to preserved from any deletion, falsification and stored safely on the long term. They are the record of the evolution of our ecosystems, of the evolution of Human societies in its environment and shall be kept available for the next generations and preserved against political or societal fluctuations. Sustainable development, predicting and monitoring future outcomes, prevention of societal and environmental risks (...), as a first necessary and crucial condition, rely on the storing and availability of such data: erasing the past makes us blind to the future.

    The United Nations Educational, Scientific and Cultural Organization (UNESCO) is a specialized agency of the United Nations (UN). Its declared purpose is to contribute to peace and security by promoting international collaboration through educational, scientific, and cultural reforms in order to increase universal respect for justice, the rule of law, and human rights along with fundamental freedom proclaimed in the United Nations Charter. Projects sponsored by UNESCO include literacy, technical, and teacher-training programmes, international science programmes, the promotion of independent media and freedom of the press, regional and cultural history projects, the promotion of cultural diversity, translations of world literature, international cooperation agreements on secure the world cultural and natural heritage (World Heritage Sites) and to preserve human rights, and attempts to bridge the worldwide digital divide. UNESCO's aim is "to contribute to the building of peace, the eradication of poverty, sustainable development and intercultural dialogue through education, the sciences, culture, communication and information". Other priorities of the organization include attaining quality Education For All and lifelong learning, addressing emerging social and ethical challenges, fostering cultural diversity, a culture of peace and building inclusive knowledge societies through information and communication. (Wikipedia). On the initiative of the United Nations Secretary-General, Global Pulse a flagship innovation on big data was launched and should provide a partner of the project within UN. Its vision is a future in which big data is harnessed safely and responsibly as a public good. Its mission is to accelerate discovery, development and scaled adoption of big data innovation for sustainable development and humanitarian action.

    The Security Council of UN, through its 24th march 2017 report, stated that "illegal attacks on sites and buildings devoted to religion, education, art, science or for historical charitable purposes or monuments may constitute , in certain circumstances and in accordance with international law a war crime and that the perpetrators of these attacks must be brought to justice" (Cf. article). Before the Security Council on Friday, 24 March 2017, Irina Bokova, Director-General of Unesco said: "The deliberate destruction of heritage is a war crime, it has become a tactic of war to undermine society in the long term, in a strategy of cultural cleansing, which is why the defense of cultural heritage is much more than a cultural issue, it is a security imperative inseparable from the defense of human lives ". She also recalled that weapons were not enough to defeat violent extremism. "Building peace also depends on culture, which requires education, prevention and the transmission of heritage." (Cf. article). Moreover, Unesco will organize on the 28th Sept 2017 a whole conference 'IPDCtalks' to highlight and elaborate on the importance of Access to Information for all sustainable development efforts around the world.
    The World Heritage Convention is an international treaty between Member States of the United Nations. It seeks to identify, protect, conserve, present and transmit to future generations cultural and natural heritage of Outstanding Universal Value. The World Heritage Convention is rooted in the recognition that cultural and natural heritage is among the priceless and irreplaceable assets, not only of each nation, but of humanity as a whole. The loss, through deterioration or disappearance, of any of these most prized properties constitutes an impoverishment of the heritage of all the peoples of the world (Unesco Guide Chap1, p.14. The Operational Guidelines define Outstanding Universal Value as being cultural and/or natural significance which is so exceptional as to transcend national boundaries and to be of common importance for present and future generations of all humanity (Paragraph 49).

    Application to Unesco World Heritage

    The guide for submitting a Unesco World patrimony proposal provides all the information for the submission. The project has to fulfil the forms and criteria imposed by Unesco to submit a proposal.
    The main difficulty with respect to usual criteria of Unesco World Heritage is the "localization" requirements: a Numerical patrimony is in a weak sens (at least can be in some cases) "de-materialized" or "unlocalized" (in the sense of cloud computation and storage, that appears to be the sustainable future development of computational resources). We will have to stress the peculiarity of an e-patrimony, of a numerical patrimony and to propose an evolution in Unesco criteria. In a first step, we will organize and found 'local server', such that the usual Unesco requirements of "localization" are fulfilled, but we already advance since this early stage, that at maturity and on the long term Unesco will have to consider to weaken its localization criteria in the case of Numerical patrimony.
    The environmental database complies with the criteria **mixed properties Heritage ** as those which satisfy part or the whole of the definitions of both cultural and natural heritage (Unesco Guide Chap1, p.24. First, it complies notably with the status of monument: "works of monumental sculpture and painting, elements features, or structures of an archaeological nature, inscriptions, and combinations of which are of Outstanding Universal Value from the point of view of history, art or science." (Unesco Guide Chap1, p.20. Second, it complies with natural Heritage defined by the World Heritage Convention as: natural features consisting of physical and biological formations or groups of such formations, which are of Outstanding Universal Value from the aesthetic or scientific point of view. Or a geological and physiographical formations and precisely delineated areas which constitute the habitat of threatened species of animals and plants of Outstanding Universal Value from the point of view of science or conservation.
    Once accepted that environmental data constitute a monument of scientific knowledge and a unique record of our natural ecosystem (of Outstanding Universal Value), the environmental database, satisfy obviously not just one, but the criterion i, ii, iii, iv, vi, viii, and ix (Unesco Guide Chap1, p.34).
    The boundaries of the environmental data backup are given in the three or four domains:

    Climate data backup: the backup realized by azimuth, the climate mirror, and other databases specialized in climate recordings to be defined. Biodiversity data backup: backup of biodiversity databases to be defined. Cultural data backup: backup of indigenous oral and written traditions databases to be defined. Health data backup: (?) backup of epidemiological, genetic and epigenetic databases to be defined.

    Proposed deadline for the submission of the project (two years of preparation is advised): december 2018

    Draft of the project

    E-team to hold the project:
    Preparing a World Heritage nomination usually requires a team approach because of the complexity of the task, the range of key stakeholders, and the range of expertise required. (Chap 2.2 ). For the moment, the kernel team of voluntaries is:

    John Baez (to be confirmed) - Mathematician with interests in Physic, Biology and Ecology - Founder of Azymuth project. Émilie Barrucand - Anthropologist - Founder of Wayanga Association Pierre Baudot - Biologist with interest in mathematic Paul Bourgine - Economist - Founder of Complex System Institute Paris and of CS-DC Uniwin UNESCO Pierre Collet - Bio-Informatician - Co-ordinator of CS-DC Uniwin UNESCO David Tanzer (to be confirmed) Software developer - Co-ordinator of Azymuth project.
    Unesco suggests some bigger team to be involved, and we ask for expert or institutional volunteers to contact us.

    Institutional partnership: Unesco suggests that official institutions like universities, and research autorities to be involved in the project. Each member of the e-team may ask for his own research institution to be an official partner of the project or propose other pertinent one. The Complex System-Digital Campus Unitwin Unesco is by default one partner that already involves more than a hundred of scientific institutions around the world. (TBA: Global Pulse ? Strasbourg University?, Aix-Marseille University?, IRD? CNRS, Inserm? ... )
    State party partnership: Unesco suggests that state party to be involved in the project (Chap 2.2 ), and we will ask for the support of French government who already took position on the topic (invitation from the President Macron to researchers, Official talk of Mr. Macron after US left Paris agreement "France will not give up the fight" "Make Our Planet Great Again" 01/06/2017), and notably the French Ministry of environment who following the request of the Minister Ségoléne Royal is already studying the support.

    Scientific expert committee for archivs
    The e-team designate one expert committee per domain of archives inside the e-team (listed above). Backuping databases: The usual peer-reviewing scientific process appears as the most efficient criteria for scientific illegibility of a backup. In consequence, the data archives are managed by a scientific committee that verifies the eligibility to the back-up on the only base of the existing peer-reviewed process without substituting to it. The committees only ensures that the data basis that are submitted for a backup are issued from a peer-reviewed scientific process, that its topic fit to the archives domain and that the storing capacity allows such a backup. Researchers that submit databases is responsible for the verification that its submission complies the open source requirement. In further development, the scientific legibility review of the committee could get involved in case of submission of non peer-reviewed databases from private companies such as energy or telecommunication companies.
    Accessing to the databases: by default, all the archives are available and open freely to consultation and downloading. On special request, the database can be restricted to a specific set of users.
    Opening to modelisation and organizing Challenges and Benchmarks: The databases will be made available to data challenges (example of challenge : Predicting next year local or global majors environmental events). The project will develop partnership with major open data contest, non-exhaustively Data Challenges (Gilles Wainrib and Stephane Mallat), CS-DC olympiades, and United Global Pulse Data for Climate Action.

    Archives of the databases - Computing resources
    In a first implementation of the project, the backup will be organized on localized and dedicated servers, and the possibility of distributed (cloud) storing will be proposed to Unesco on the long term. The CS-DC, via Pierre Collet, the Icube lab and Strasboug University and thanks to private donation to the Strasbourg University Fondation UNISTRA, already provided 40 Terabytes in RAID 5 with FTP access. We will propose to settle the first backups on this site using this infrastructure and process. Interface for the depository: a web interface for the depository will be developed, this may be achieved in partnership and using the efforts already provided by the climate mirror project (To be done).

    Financial support
    The main costs of the project concerns data servers, their maintenance with a high rate transfer availability, and the development of an interface for the depository:

    Private donation campaign : the donation of individuals and private companies are welcome. They can be easily (few minutes) made via the Strasbourg University Fondation UNISTRA, by filling this DONATION FORM with specifyed "CS-DC Unesco UniTwin" as the recipient of the donation. Partners funding Unesco and other institutional partners will be asked to participate on the financial aspects (TBA). French government proposed also to support this project, and is currently studying the proposition (to ask again cf. the request of the Minister Ségoléne Royal).

  • We, stakeholders of Open Access scientific publishing, hereby claim that:




    This Call was drafted on the campus Jussieu in Paris by a French group comprising researchers and scientific publishing professionals working together in Open Access and Public Scientific Publishing task forces of BSN (Bibliothèque scientifique numérique, or Digital Scientific Library).

    This Call is aimed at scientific communities, professional associations and research institutions to promote a scientific publishing open-access model fostering bibliodiversity and innovation without involving the exclusive transfer of journal subscription monies to APC payments.
    Jussieu Call for Open science and bibliodiversity

    As asserted in the Amsterdam Call released in 2016, Open Access to scientific publishing is at a crossroads. After several years of an exacting struggle aiming at persuading somewhat skeptical stakeholders, Open Access has now won strong support and a rapid shift of the scientific communication system to an Open Access publishing model can be expected. “The time for talking about open access is now past”. “The time for talking about open access is now past”.

    The means to achieve the goal of Open Access are yet to be discussed. We believe that the issue of business models has to be refocused in the broader perspective of the editorial processes and methods upon which research and innovation will rely in the future. and that they may only develop for the benefit of a very broad bibliodiversity.

    We find it necessary to foster an Open Access model that is not restricted to a single approach based on the transfer of subscriptions towards APCs (publication fees charged to authors to allow free access to their articles). Such an approach would hamper innovation and otherwise would slow if not check the advent of bibliodiversity. Therefore, we adhere to the Joint Statement of UNESCO and the Confederation of Open Access Repositories (COAR) on Open Access which highlights all the difficulties caused by this single model.

    Our goal is thus to develop and implement alternative models matching the aims of open science by asserting the need of supporting innovation for a thorough renewal of publishing functions as proclaimed by the Association of European Research Libraries (LIBER) and the International Council for Science (ICSU).
    We, stakeholders of Open Access scientific publishing, hereby claim that:

    1 Open Access must be complemented by support for the diversity of those acting in scientific publishing – what we call bibliodiversity – putting an end to the dominance of a small number among us imposing their terms to scientific communities; 2 the development of innovative scientific publishing models must be a budget priority because it represents an investment into services meeting the genuine needs of researchers in our digital age; 3 experiments should be encouraged in writing practices (publishing associated data), refereeing (open peer-reviewing), content editorial services (beyond-pdf web publishing) and additional services (text mining); 4 the research evaluation system should be thoroughly reformed and adapted to the practices of scientific communication; 5 more investment efforts in open source tools upon which these innovative practices are based should be made and coordinated; 6 the scientific community needs a secure and stable body of law across different countries to facilitate the availability of text mining services and thus strengthen their use; 7 the scientific communities must be able to access national and international infrastructures which guarantee the preservation and circulation of knowledge against any privatization of contents. Business models should be found which preserve their long-term continuity; 8 priority should be given to business models that do not involve any payments, neither for authors to have their texts published nor for readers to access them. Many fair funding models exist and only require to be further developed and extended: institutional support, library contributions or subsidies, premium services, participatory funding or creation of open archives, etc. We endorse the clear message to the scientific community at large released by the League of European Research Universities (LERU): Research funding should go to research, not to publishers! This is why current journal subscription spendings should be changed into investments enabling the scientific community to regain control over the publishing system and not merely into new spendings only earmarked to pay the publication fees for researchers to commercial publishers.

    We call on creating an international consortium of stakeholders whose primary aim should be to pool local and national initiatives or to build an operational framework to fund open access publishing, innovation and sharing of resulting developments. We call on research organizations and their libraries to secure and earmark as of now a share of their acquisition budgets to support the development of scientific publishing activities, which are genuinely open and innovative, and address the needs of the scientific community.

    Contributors :

    Serge BAUIN; Céline BARTHONNAT; Christine BERTHAUD; Thierry BOUCHE; Francois CAVALIER; Gregory COLCANAP; Odile CONTAT; Nathalie FARGIER; Thierry FOURNIER; Anne-Solweig GREMILLET; Frédéric HÉLEIN; Odile HOLOGNE; Emmanuelle JANNES-OBER; Jacques LAFAIT; Jean François LUTZ; Sandrine MALOTAUX; Jacques MILLET; Pierre MOUNIER; Jean-Francois NOMINÉ; Christine OKRET-MANVILLE; Christine OLLENDORFF; Sébastien RESPINGUE-PERRIN; Julien ROCHE; Laurent ROMARY; Dominique ROUX; Joachim SCHOPFEL; Bernard TEISSIER; Armelle THOMAS; Céline VAUTRIN

    Signing Institutions: (TBA)

    The signing process of the Call is currently underway. The list of signing institutions will be added shortly.
    The signatories’names, institution title and logo will then be displayed on this page as their approval is received.

    le Conseil scientifique de l'INSMI le CA de la SMAI le First Consortio Assembly from Ibero-Americana and the Caribbean

    How to sign this Call :

    Scientific communities, research institutions and scientific and technical information professional associations and organizations are called to support the Jussieu Call.

    How to do this : The President/chairperson of the signatory sends an email indicating the agreement of his/her institution together with an official web logo and institutional title
    to : JussieuCall at gmail dot com

  • from June 28 to July 4, 2018 at the Palais des Congrès in Arcachon, France.




    Welcome to Curves and Surfaces

    Welcome to 9th International Conference on Curves and Surfaces, organised by SMAI-SIGMA.

    The conference will take place from June 28 to July 4, 2018 at the Palais des Congrès in Arcachon, France.

    Registration, accomodation and abstract submission are now open.

    Conference topics

    Overall theme: “Representation and Approximation of Curves and Surfaces and Applications” with subtopics (in alphabetical order):

    Approximation theory Computer-aided geometric design Computer graphics and visualisation Computational geometry and topology Geometry processing Image and signal processing Interpolation and smoothing Mesh generation, finite elements and splines Scattered data processing and learning theory Sparse and high-dimensional approximation Subdivision, wavelets and multi-resolution methods

    as well as related applications in manufacturing, mechanics, solid modelling, terrain modelling, oceanography, geosciences, life sciences ...

    Invited speakers

    Alexander Bobenko (Technische Universität Berlin) Emmanuel Candes (Stanford University) Maria Charina (Universität Wien) Elaine Cohen (University of Utah at Salt Lake City) Philipp Grohs (Universität Wien) Frances Kuo (University of South Wales) Mauro Maggioni (Johns Hopkins University) Jorg Peters (University of Florida at Gainsville) Amit Singer (Princeton University) Max Wardetzky (Georg-August Universität Goettingen)


    Constrained approximation (Dany Leviatan, Tel Aviv University) High dimensional approximation (Vladimir Temlyakov, University of South Carolina) Applications in energy industry (Christian Gout, INSA Rouen) Isogeometric Analysis (Giancarlo Sangalli, Universita di Pavia ; Mario Kapl, RICAM) Mathematical aspects of 3D printing (Georg Muntingh, Sintef Oslo) Advances in radial basis approximation (Thomas Hangelbroek, University of Hawaii at Manoa) Topological data analysis and learning (Steve Oudot, INRIA Saclay) Shape processing (Martin Rumpf, Universität Bonn) PDE and variational methods for geometry processing for images (Carola-Bibiane Schoenlieb, Cambridge University ; Simon Masnou, Université de Lyon) Advances on Prony’s methods (Stefan Kunis, Universität Osnabrueck)

    Call for contributions

    We welcome contributions as oral presentations and posters. See the submission page for more details, and the important dates page for submission deadlines.

    Three best poster awards with prizes will be awarded.


    Jean-Francois Aujol (Université de Bordeaux) Jean-Daniel Boissonat (INRIA Sophia) Albert Cohen (Université Pierre et Marie Curie) Tom Lyche (University of Oslo) Marie-Laurence Mazure (Université Grenoble Alpes) Quentin Mérigot (Université Paris Saclay) Gabriel Peyré (CNRS and Ecole Normale Supérieure) Larry Schumaker (Vanderbilt University)

    Scientific committee

    Peter Binev (Univeristy of South Carolina ) Annalisa Buffa (EPFL) Ron Devore (Texas A&M University) Oleg Davydov (Universität Giessen) Jalal Fadili (ENSI Caen) Remi Gribonval (INRIA Rennes) Gitta Kutyniok (Technische Universität Berlin) Dany Leviatan (Tel Aviv University) David Levin (Tel Aviv University) Carla Manni (Universita di Roma) Edouard Oudet (Université Grenoble Alpes) Konrad Polthier (Freie Universität Berlin) Helmut Pottmann (Technische Universität Wien) Ulrich Reif (Technische Universität Darmstadt) Tomas Sauer (Universität Passau) Grady Wright (Boise State University) Michael Floater (University of Oslo)

    Supported by

    The conference is organised by SMAI-SIGMA an activity group of Société de Mathématiques Appliquées et Industrielles (SMAI) in collaboration with the following institutions:

    Capture du 2017-12-18 19-37-14.png

    Previous editions

    Paris 2014 Avignon 2010 Avignon 2006 St Malo 2002 St Malo 1999 Chamonix 1996 Chamonix 1993 Chamonix 1990
  • Geo-Sci-Info

    Vinberg's theory of homogeneous convex cones: developments and applications
    D. Alexeevsky


    IITP, Russia

    The talk is a review of developments and applications of selected basic ideas, results and notions, presented by E.B. Vinberg in his papers about classification of homogeneous convex cones. We confine ourselves to only three topics:

    Theory of left symmetric algebras (Vinberg–Koszul algebras) and Hessian manifolds. Information geometry , i.e. the geometry of manifolds of probability measures , developed mostly by N.N. Chentsov and S-I Amary and based on ideas by R.A.Fisher, C.R.Rao, C. Shannon and S. Kullback. Supergravity. Application of the theory of matrix T-algebras by Vinberg for description of so called special geometries (very special real geometry, special Kähler geometry and special quaternionic Kähler geometry (affine and projective)) which arise as matter multiplets in Supersymmetry and Supergravity in spacetime dimension d=6,5,4,3.
  • Geo-Sci-Info


    History This conference can be considered a continuation of previous meetings on Geometry and Mathematical Physics which took place in Bulgaria - Zlatograd (1995) and Varna (1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016 and 2017).

    "Geometry" in the title refers to modern differential geometry of real and complex manifolds with some emphasis on curves, sigma models and minimal surface theory, "Integrability" to either the integrability of complex structures or classical dynamical systems of particles, soliton dynamics and hydrodynamical flows presented in geometrical form, and "Quantization" to the transition from classical to quantum mechanics expressed in geometrical terms.

    The overall aim is to bring together experts in Classical and Modern Differential Geometry, Complex Analysis, Mathematical Physics and related fields to assess recent developments in these areas and to stimulate research in related topics.

    The workshop will take place in a stimulating environment, which combines a virgin natural landscape with tourist comfort and the unexpected neighbourhood of an ancient historical past. All this in a splendid part of the northern Bulgarian Black Sea coast in the vicinity of Varna.

    Organizing Committee
    Ivaïlo M. Mladenov (Sofia) and Akira Yoshioka (Tokyo)

    Scientific Secretaries
    Mariana Hadzhilazova (Sofia) and Vladimir Pulov (Varna)

    Advisory Committee
    Vladimir Kisil (Leeds), Guowu Meng (Hong Kong), John Oprea (Cleveland),
    Magdalena Toda (Lubbock), Abraham Ungar (North Dakota) and Alexandar Yanovski (Cape Town)

    Standing Committee
    Vladimir Gerdjikov(Sofia), Metin Gürses (Ankara), Andrei Ludu (Daytona),
    Gregory Naber (Philadelphia), Izu Vaisman (Haifa) and Anatol Odzijewicz (Bialystok)

    Scientific Programme
    As previous years we plan to have a few Lecture Courses of 3--5 talks each and Plenary Talks which will be delivered by:


    Vladimir V. Kisil - The Heisenberg Group and the Group SL2(R) : A Surviving Pack for Everyone
    *Ivaïlo M. Mladenov - The Many Faces of Elastica

    Plenary Talks


    and at least 30 minute talks by the other participants

    Pavel Bibikov - On Effective Classification of PDEs with Algebraic Coefficients TBA

    **Proceedings **
    The Proceedings will be published after peer review process which is carried out by the Editors of the Proceedings Series and the Editorial Board of Journal of Geometry and Symmetry in Physics. Since the begining of 2015 all Proceedings are a part of Euclid

    Registration Fee
    There will be a flat registration fee of €440/ €540 for a double/single room accomodation. PhD students are entitled up to a 50% reductions of the above fees. Besides the room this fee covers three meals per day, refreshments and a copy of the Conference Proceedings volume. Payment in US dollars and Bulgarian Levs is accepted at current exchange rates. Please notice that this fee should be paid in cash upon arrival and that no credit cards, personal cheques, bank drafts or travelers cheques can be accepted.

    Please, complete and send your Registration Form. If you wish just to receive information about the future meetings, please register here. Deadline to submit registration forms and abstracts is 15th May 2018.

    In the Koral hotel in Sts. Constantine and Elena (hotel Koral is listed under number 26 on the map). It is assumed that 1 June and 8 June are the arrival, respectively the departure days.
    Please, notice that there is not a local organizing Committee in Varna and earlier arrival will create a lot of problems. However the stay after the Conference can be arranged.


    International Conference on Geometry, Integrability and Quantization, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, 1113 Sofia, BULGARIA.
    Tel: ++ 3592--979 26 37, Fax: ++ 3592--872 37 87

    E-mail Addresses:
    Ivaïlo M. Mladenov -- mladenov[at]
    Akira Yoshioka -- yoshioka[at]

    Related conferences

    38th Winter School Geometry and Physics, 13 - 20 January 2018 (Srni, Czech Republic) 12th International Young Researchers Workshop on Geometry, Mechanics and Control, 22-24 January 2018 (Padova, Italy) Dynamics and Integrability of Nonholonomic and other Non-Hamiltonian Systems, 24-27 January 2018 (Padova, Italy) XXXVI Workshop on Geometric Methods in Physics, 01 -07 July 2018 (Bialowieza, Poland) Tenth Jubilee Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, 20-25 June 2018 (Albena, Bulgaria)


    baslogo.jpg TUSlogo.gif
    Design by M. Hadzhilazova, E-mail: murryh [at] All rights reserved.

  • 1st Feb. 2018, Centre de Morphologie Mathématique, Ecole des Mines de Paris, France



    41ème journée ISS France, morphologie mathématique et stéréologie

    La prochaine Journée d'Etude ISS France aura lieu le

    jeudi 1 février 2018
    à l’Ecole des Mines de Paris
    60 boulevard Saint-Michel, Paris 6
    Salle V107

    La date limite pour l'envoi des communications est le 20 janvier 2018.
    Les propositions sont à envoyer sous forme de court résumé (une demi-page)
    par mail à la présente adresse.

    Pour ceux qui ne les connaissent pas encore, les journées d’étude de l’ISS
    sont l’occasion de rassembler les différents acteurs de l’analyse des
    images numériques, de la stéréologie, de la géométrie stochastique et de
    leurs applications et connexions.

    La volonté a été affirmée depuis de nombreuses années de faire des
    journées d’étude de l’ISS France un lieu de rencontre, d’échange et
    d’expérimentation réellement pluridisciplinaire qui puise ses forces dans
    l’ensemble du patrimoine intellectuel actuel.

    Le point d’ancrage reste délibérément l’image et les techniques, sciences,
    applications et arts qui s’y intéressent.

    Vous trouverez en pièce jointe le programme de la dernière journée
    d'étude en février 2017 ; vous pourrez y découvrir les thèmes et rubriques
    classiquement abordés lors de ces journées :

    La Session Méthodes est principalement axée sur les nouveaux paradigmes
    en traitement des images, La session Art et Image aborde les questions de valeur des images au
    sens large, Les sessions Applications parcourent les principaux travaux
    réalisés dans les domaines des BioSciences, des Sciences des Matériaux, de
    la Géographie Mathématique,...

    Cette année encore, nous espérons que la journée d’étude de l’ISS France
    sera l’occasion de nouveautés !
    Dans l'attente de vos propositions, nous restons à votre disposition pour
    toute information complémentaire,

    Pour ISS France,

    Corinne Lagorre** & Bruno Figliuzzi***
    ** Université Paris Est Créteil, LISSI 61 avenue du Gal de Gaulle 94000
    Créteil corinne.lagorre [at]

    *** Mines ParisTech, Centre de Morphologie Mathématique 35 rue Saint-Honoré
    77305 Fontainebleau cedex

  • Orbituary Friday January 12th 2018


    Dear all,

    Profound sadness on the passing of Professeur Jean-Louis Koszul (1921-2018+), Friday January 12th 2018, Geometer, Henri Cartan's PhD student, member of Bourbaki, who honored us with his presence at Mines GSI'13 conference for Hirohiko Shima keynote on Geometry of Hessian Structures related to Koszul forms and Koszul-Vinberg Characteristic Function.
    His works on convex homogeneous cones are at the foundation of Information Geometry theory.

    DOWNLOAD article in tribute to Koszul - Jean-Louis Koszul et les structures élémentaires de la Géométrie de l'Information - F. Barbaresco

    Koszul's papers:
    [A] Koszul, J.L. Sur la forme hermitienne canonique des espaces homogènes complexes. Can. J. Math. 1955, 7, 562–576.
    [B] Koszul, J.L. Exposés sur les Espaces Homogènes Symétriques; Publicação da Sociedade de Matematica de São Paulo: São Paulo, Brazil, 1959.
    [C] Koszul, J.L. Domaines bornées homogènes et orbites de groupes de transformations affines. Bull. Soc. Math. Fr. 1961, 89, 515–533.
    [D] Koszul, J.L. Ouverts convexes homogènes des espaces affines. Math. Z. 1962, 79, 254–259.
    [E] Koszul, J.L. Variétés localement plates et convexité. Osaka. J. Math. 1965, 2, 285–290.
    [F] Koszul, J.L. Déformations des variétés localement plates. Ann. Inst. Fourier 1968, 18, 103–114.
    [G] Koszul, J.L. Trajectoires Convexes de Groupes Affines Unimodulaires. In Essays on Topology and Related Topics; Springer: Berlin, Germany, 1970; pp. 105–110.

    Attached: photo of his last interview for 50th years birthday of "Institut Fourier" in Grenoble:
    May his soul rest in peace.

    F. Barbaresco
    GSI'17 Co-chairman

  • 14-18 mai 2018 Paris (France)





    7ème édition des rencontres annuelles entre les membres du GdR "Géométrie stochastique". Le GdR a vocation à réunir les chercheurs qui étudient d'un point de vue théorique ou appliqué des modèles spatiaux aléatoires. Les spécialités représentées au sein du groupe vont des probabilités et la statistique à la géométrie, la théorie ergodique, l'analyse d'images, l'algorithmique et l'astrophysique. Pour plus d'information:

    Registration is free but mandatory

    Young researchers
    Financial help for travel and accomodation can be given to young researchers, PhD students in priority, upon request. See the registration page

    **Master class (Monday 14 and tuesday 15) **

    Tobias MULLER, Université de Groningen Bartek BLASZCZYSZYN, INRIA/ENS Paris

    Talks (From wednesday 16 to friday 18 at 1pm)

    Denis ALLARD, INRA Avignon Erik BROMAN, Chalmers University, Gothenburg, Suède Nicolas BROUTIN, Université Pierre et Marie Curie Elie CALI, Orange Agnès DESOLNEUX, ENS Cachan Damien GAYET, Université Grenoble 1 Emmanuel JACOB, ENS Lyon Eva JENSEN, Aarhus University, Danemark Christian LANTUEJOUL, Mines Paris Tech Régine MARCHAND, Université de Lorraine Domenico MARINUCCI, Université Rome Tor Vergata Dieter MITSCHE, Université de Nice Julien RANDON-FURLING, Université Paris 1 Maurizia ROSSI, Université Paris Descartes

    Last meetings


    Bartlomiej Blaszczyszyn ( ENS / Inria ) David Coupier (Université de Valenciennes) Yann Demichel (Université Paris Nanterre) Nathanaël Enriquez (Université Paris-Sud) Anne Estrade (Université Paris Descartes) Raphaël Lachièze-Rey (Université Paris Descartes)

    departments involved

    Laboratoire MAP5 Modal'X DI ENS ( CNRS / Inria / ENS, UMR 8548)


  • Geo-Sci-Info

    Geometric Analysis of Multimedia Data: Geometry as a Paradigm for Machine Learning and Data Mining in Multimedia Systems

    Call for Papers

    Nowadays, multimedia systems require processing, representation, storage, and transmission of large amount of multidimensional digital information, possibly sampled from nonlinear manifolds. On top of that, multimedia applications involve the computer-controlled integration of text, graphics, images/video, and audio represented in digital form with the goal of letting the user navigate, interact, create, and communicate insights inferred from sources of raw information. The complex tasks involved in such processes lead to a strong demand for multimedia data analysis and information extraction/retrieval.

    In this context, manifold learning techniques have been applied to embed nonlinear data in lower dimensional spaces for subsequent analysis. The result allows a geometric interpretation of information with relevant consequences regarding data topology, sampling theory, similarity computation, pattern recognition, and, more recently, deep learning. Yet in the direction of using geometry as a paradigm for machine learning and data mining, we shall include the geometric data analysis and the development of tools for studying geometric features of data through topological data analysis.

    The main goal of this special issue is to explore geometric concepts for analysis, data representation and integration, information extraction, and retrieval from multimedia systems. We encourage submissions that combine geometric concepts, data representation techniques, and machine and statistical learning methods, for extracting meaningful information from high-dimensional data spaces. Review articles that describe the current state of the art in geometric analysis of multimedia data are welcome as well.

    Potential topics include but are not limited to the following:

    Geometric analysis of deep hierarchical structures in multimedia Generative models and manifolds for multimodal information retrieval Data models integrating geometry, multivariate statistics, and sampling theory Topological data analysis for studying geometric features of data Multimedia data mining and analysis based on geometry and topology Multimedia data analysis based on manifold learning Geometric based approaches for metric learning Geometry as a paradigm to discuss deep learning algorithms in multimedia applications

    Authors can submit their manuscripts through the Manuscript Tracking System at
    Submission Deadline Friday, 10 August 2018
    Publication Date December 2018

    Papers are published upon acceptance, regardless of the Special Issue publication date.

    Lead Guest Editor
    Gilson A. Giraldi, National Laboratory for Scientific Computing, Petrópolis, Brazil

    Guest Editors

    Carlos E. Thomaz, University Center of FEI, São Bernardo do Campo, Brazil Jaime S. Cardoso, University of Porto, Porto, Portugal J. Dinesh Peter, Karunya Institute of Technology & Sciences, Coimbatore, India
  • Geo-Sci-Info

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    L'objectif de cette école est de donner aux étudiants et jeunes chercheurs de la sous-région Afrique centrale une formation de qualité en topologie algébrique et en géométrie, sur des sujets faisant l'objet de recherches actuelles dans les domaines de la recherche fondamentale en mathématiques, de la physique mathématique et de la robotique.
    Quoique portant sur des sujets avancés et actuels, les cours seront conçus pour être accessibles aux jeunes mathématiciens. Les cours de la première semaine viseront en particulier à donner des bases solides et systématiques sur les sujets concernés, de façon à les rendre accessibles à tous les participants, même sans connaissances préalables au-delà des bases classiques en algèbre, topologie ou géométrie.
    Le but principal de l'école est de les motiver au maximum et de leur donner les outils nécessaires pour leurs futures recherches. L'un des objectifs fondamentaux de cette école sera ainsi de donner aux participants de la sous-région des pistes de recherches d'actualité et intéressantes pouvant déboucher sur des résultats susceptibles de contribuer au développement de la sous-région.
    La majorité des cours qui y seront dispensés porteront sur la topologie algébrique. Plus précisément, on s'intéressera aux espaces de configurations qui apparaissent en mathématique, en physique classique, en statistique, en robotique, en biologie, en mécanique classique et en mécanique statistique. Seront principalement abordées les notions suivantes: Outils de Topologie algébrique, Espaces de configuration, Opérades algébriques, Opérade des petits disques, Géométrie de Poisson et Quantification géométrique. Ces notions seront présentées dans un langage « simple », en une série de 7 cours de 6 heures avec travaux dirigés, chacun par des spécialistes reconnus. Les après-midi seront essentiellement réservés aux travaux dirigés, aux exposés et échanges entre participants.


    Michel Nguiffo Boyom (Université de Montpellier) Cristina Costoya (Universidade da Coruna, Espagne) Pascal Lambrechts (Université de Louvain-La-Neuve) Eugène Okassa (Université Marien Ngouabi, Congo) Frédéric Patras (CNRS - Université de Nice, France) Paul Arnaud Songhafouo Tsopméné (University of Regina, Canada)

    Coordinateurs :

    Bitjong NDOMBOL (Université de Yaoundé 1)
    Frédéric PATRAS (CNRS - Université de Nice)

    Comité d'organisation local :

    Calvin TCHEKA (Cameroun)
    Charles AWONO ONANA (Cameroun)
    Joseph DONGOH (Cameroun)
    Bitjong NDOMBOL (Cameroun)
    Anne Marie TIAYA (Cameroun)

    Comité scientifique :

    Cristina COSTOYA (Espagne)
    Bitjong NDOMBOL (Cameroun)
    Michel NGUIFFO BOYOM (France)
    Eugène OKASSA (Congo)
    Frédéric PATRAS (France)

    L'école est soutenue par:

    CIMPA/ICPAM (Centre International de Mathématiques Pures et Appliquées/International Center for Pure and Applied Mathematics)

    IMU-CDC (Union mathématique internationale, Commission for developing countries)

    Université de Yaoundé 1

  • Application dead line : 30 april 2018


    Prix de Thèse Systèmes Complexes 2018


    Dans le cadre de leur politique d’encouragement de l’enseignement et de la recherche, l’Institut des Systèmes Complexes de Paris Île-de-France (ISC-PIF) et le Réseau National des Systèmes Complexes (RNSC) organisent la deuxième édition du Prix de Thèse Systèmes Complexes en juin 2018.

    Ce prix de thèse a pour objectif de mettre à l’honneur la recherche dans le domaine des systèmes complexes et de distinguer les travaux de jeunes chercheurs·euses particulièrement prometteurs·euses.
    Le concours

    Ce concours est ouvert à toute personne, quelle que soit sa nationalité, ayant soutenu une thèse de doctorat dans une école doctorale française après le 1er janvier 2017.

    Les candidats·tes doivent remplir leur candidature en ligne à partir du 1e mars 2018 et avant le 30 avril 2018 à minuit :

    Le jury

    Les dossiers seront sélectionnés au cours du mois de mai 2018 par un jury interdisciplinaire composé de personnalités reconnues dans le domaine des systèmes complexes. Les critères pris en compte par le jury pour sélectionner les lauréats·tes sont, notamment, l’importance et l’originalité des contributions dans le domaine des systèmes complexes, la qualité du manuscrit et l’interdisciplinarité éventuelle des travaux.

    Les finalistes seront invités·ées à venir présenter leurs travaux accompagnés·ées de leur(s) directeur·trices(s) de thèse lors de la cérémonie de remise des prix, en juin 2018.
    Eléments demandés

    Pour remplir le dossier de candidature, vous aurez besoin des éléments suivants :

    – le lien du site sur lequel est publié/stocké votre thèse. Votre thèse doit pouvoir être téléchargée en format pdf.
    – Un CV, 2 pages maximum, en anglais ou en français.
    – Une lettre de motivation : 2 pages maximum, en anglais ou en français. La lettre de motivation doit expliciter notamment le positionnement de la thèse vis-à-vis des principaux “objets” et “questions” définis par la feuille de route systèmes complexes, ainsi que les aspects interdisciplinaires éventuels de la thèse et les apports pour l’étude des systèmes complexes
    – Le rapport du jury de thèse et pré-rapports des rapporteurs.
    – Le résumé de la thèse. En deux pages maximum, accessible à des non spécialistes du sujet traité.

    ISC-PIF, 113 rue Nationale
    Paris 13

    Cet événement Prix de Thèse Systèmes Complexes a été réalisé avec le soutien du DIM Problématiques transversales aux Systèmes Complexes.

  • Geo-Sci-Info


    Bernoulli Lecture - What is Probability?
    27 March 2018 - CIB - EPFL - Switzerland
    17:15 - 18:15
    Room : BCH 2201

    Mikhail Gromov, Université Paris-Saclay

    Download the PDF of the lecture

    The success of probability theory in mathematics and in theoretical physics is due not so much to its measure-theoretic foundation, but rather it is because it exploits and enhances the symmetries of the structures it applies to. We shall describe in this lecture two alternative approaches to the concept of probability, where

    the first one is motivated by the ongoing revision of the set-theoretic language in mathematics, as it is being systematically superseded by the category-theoretic one;

    the second approach is motivated by the needs of biology and linguistics, where the structures do not possess symmetries of the kind physical structures enjoy.


  • International workshop in honor of Dominique Jeulin Peninsula of Oléron (Atlantic coast, France), June 17-22, 2018


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    This international workshop is organized in the honor and in the presence of Dominique Jeulin, after he retired from École des Mines in 2016. The workshop aims to promote ideas and establish connections between researchers working in the wide field of mechanics and physics of heterogeneous media. The main themes of the workshop cover the theoretical and numerical modeling of microstructures and of their electrical, mechanical and transport properties, in solid mechanics and material science. Sessions will emphasize all main topics beloved by Dominique, ranging from probabilistic models, multiscale structures, microstructure evolution, image analysis for materials, homogenization, stochastic analysis, fracture and rupture processes, transport properties of nano-structures, localization in non-periodic media, percolation theory, fuel cell technology and the problem of triple percolation, electric & magnetic properties, optical properties, computational methods.

    Preliminary programme (PDF)

    François Willot and Samuel Forest (Mines ParisTech). To contact us, write to francois.willot [at]

    Participants will have the opportunity to submit a paper for publication in a special issue of a Internationl Journal of Solids and Structures or Image Analysis and Stereology (submission due on November 1, 2018). Please let us know (francois.willot [at] if you plan to submit an article to this journal.

    Special book
    On this special occasion, a book at Presses de l'école des Mines will be published, with special articles that are of a scientific or “sentimental” nature. You are welcome to contribute to the book. The format is free, but a latex tample is available here.

    The workshop will be held at the CNRS village La Vieille Perrotine, 140, route des Allards, 17310 Saint Pierre d'Oléron, in the Île d'Oléron, France (Google map link).

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    Oléron, the home of Pierre Loti, is a 30 km-wide island (nowadays linked to the coast of France by a bridge), made of small scattered villages, beaches and pinèdes (pine forests), famous for its palourdes (local clams). Further information is available here.

    A shuttle (KEOLIS company) will link bring particpants from both the train station and airport of La Rochelle to Oléron on Sunday 17, late afternoon (train coming from Paris-Montparnasse, arriving at 16:16 and plane coming from Paris-Orly, arriving at 16:45). There will also be a shuttle for La Rochelle on June 22, after lunch.

    To reach La Rochelle, you can take the train from e.g. Paris Gare Montparnasse. The two main Paris airports are Charles de Gaulle (CDG) and Orly (ORY). RER line B (from CDG) or Orlybus (from Orly) brings you to Denfert-Rochereau station, and metro line 6 from Denfert-Rochereau to Montparnasse train station in Paris.

    At the moment (May 23), French rail workers continue to announce a strike for June 17 and June 22. If you have difficulties in finding how to come to Oléron or La Rochelle, or you plan to come by car and may be able to take participants with you, please let us know (francois.willot [at]

    Participants to the workshop will be accommodated inside the CAES (simple CNRS village with single rooms). All meals will be provided inside the village. The 90 acres village includes two ponds, a forest area and offers a direct access to the seashore. It is located in the protected area of the marsh of Moëze, near Fort Royer and Fort Boyard oyster farms. See this link for more information.

    A banquet will be held on thursday (June 21, 2018).

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    It is not possible to regiser anymore.

    Important dates
    February 15, 2018: deadline for abstract submission (talk or poster).
    March 13, 2018: notification of acceptance.
    June 17-22, 2018: workshop.

  • Geo-Sci-Info

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    This website is the home for the IHP semester “The Mathematics of Imaging” that will take place between January to April 2019.

    You can also access the monthly seminar website from here.


    Registration is free but mandatory to be able to participate to one (or to all!) of these conferences and to the pre-school.


    All the conferences will take place at IHP.

    Variational methods and optimization in imaging, February 4th-8th 2019. Statistical Modeling for Shapes and Imaging, March 11th-15th 2019. Imaging and machine learning, April 1st-5th 2019.

    CIRM pre-school

    January 7-11th 2019, CIRM pre-school for PhD students and postdocs.
    Monthly seminar on imaging sciences

    More information here.
    High-school and general audience activity


    Jean-François Aujol (Bordeaux). Julie Delon (Paris 5) Agnès Desolneux (CNRS and ENS Cachan) Jalal Fadili (ENSICAEN) Bruno Galerne (Paris 5) Gabriel Peyré (CNRS and ENS)

    Scientific committee

    Coloma Ballester (Pompeu Fabra Univ., Spain) Andrea Bertozzi (UCLA, USA) Laure Blanc-Feraud (CNRS, Nice Sophia Antipolis Univ., France) Donald Geman (Johns Hopkins Univ., USA) Stephane Mallat (ENS Ulm, France) Simon Masnou (Univ. Lyon 1, France) Jean-Michel Morel (ENS Cachan, France) David Mumford (Brown University, Providence,USA) Mila Nikolova (CNRS, ENS Cachan, France) Joachim Weickert (Saarland Univ., Germany)


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  • June 25-29 2018 - Intitut Henri Poincaré, Paris, France


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    This Summer School will consist in two courses given by professors Sergey Bobkov (Minneapolis) and Mokshay Madiman (Delaware) on Information Theory and Convex Analysis. The aim is to bring researchers from different communities (Probability, Analysis, Computer science) in the same place.
    Participation of postdocs and PhD students is strongly encouraged. The school has some (limited number of) grants for young people (see the "registration" link above).

    Sergey Bobkov (Minneapolis) : Strong probability distances and limit theorems
    Abstract: The lectures explore strong distances in the space of probability distributions, including total variation, relative entropy, chi squared and more general Renyi/Tsallis informational divergences, as well as relative Fisher information. Special attention is given to the distances from the normal law. The first part of the course is devoted to the general theory, and the second part to the behavior of such informational distances along convolutions and associated central limit theorem.

    Mokshay Madiman (Delaware): Entropy power and related inequalities in continuous and discrete settings
    Abstract: The lectures explore the behavior of Renyi entropies of convolutions of probability measures for a variety of ambient spaces. The first part of the course focuses on Euclidean spaces, beginning with the classical Shannon-Stam entropy power inequality and the closely related Brunn-Minkowski inequality, and developing several of the generalizations, variants, and reversals of these inequalities. The second part of the course focuses on discrete abelian groups, where one sees close connections to additive combinatorics.

    Related Materials

    (1) Survey on entropic limit theorems (M. Madiman) :

    Lecture 1. Introduction - What is information theory? The first question that we want to address is: “What is information?” Although there are several ways in which we might think of answering this question, the main rationale behind our approach is to distinguish information from data. We think of information as something abstract that we want to convey, while we think of data as a representation of information, something that is storable/communicable. This is best understood by some examples. Lecture 2. Basics / law of small numbers. Due to scheduling considerations, we postpone the proof of the entropic central limit theorem. In this lecture, we discuss basic properties of the entropy and illustrate them by proving a simple version of the law of small numbers (Poisson limit theorem). The next lecture will be devoted to Sanov’s theorem. We will return to the entropic central limit theorem in Lecture 4. Lecture 3. Sanov’s theorem. The goal of this lecture is to prove one of the most basic results in large deviations theory. Our motivations are threefold: 1. It is an example of a probabilistic question where entropy naturally appears. 2.The proof we give uses ideas typical in information theory. 3. We will need it later to discuss the transportation-information inequalities (if we get there). Lecture 4. Entropic CLT (1). The subject of the next lectures will be the entropic central limit theorem (entropic CLT) and its proof. Lecture 5. Entropic CLT (2). The goal of this lecture is to prove monotonicity of Fisher information in the central limit theorem. Next lecture we will connect Fisher information to entropy, completing the proof of the entropic CLT. Lecture 6. Entropic CLT (3). In this lecture, we complete the proof of monotonicity of the Fisher information in the CLT, and begin developing the connection with entropy. The entropic CLT will be completed in the next lecture. Lecture 7. Entropic CLT (4). This lecture completes the proof of the entropic central limit theorem. Lecture 8. Entropic cone and matroids. This lecture introduces the notion of the entropic cone and its connection with entropy inequalities. Lecture 9. Concentration, information, transportation (1). The goal of the next two lectures is to explore the connections between concentration of measure, entropy inequalities, and optimal transportation. Lecture 10. Concentration, information, transportation (2) Recall the main proposition proved in the previous lecture, which is due to Bobkov and Götze (1999).

    (2) A survey on forward and reverse entropy power inequalities, 2017.


    Nathael Gozlan Cyril Roberto Paul-Marie Samson.


    Institut Henri Poincaré (IHP) Laboratoire d'Analyse et de Mathématiques Appliquées (LAMA) Labex Bézout Labex MME-DII

  • Cargèse International School 2018 - August 21-31, 2018


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    The main goal of this program is to gather the community of researchers working on questions that relate in some way statistical physics and high dimensional statistical inference. The format will be several (~10) 3h introductory lectures, and about twice as many contributed invited talks. The topics include:

    Energy/loss landscapes in disordered systems, machine learning and inference problems Computational and statistical thresholds and trade-offs Theory of artificial multilayer neural networks Rigorous approaches to spin glasses and related models of statistical inference Parallels between optimisation algorithms and dynamics in physics Vindicating the replica and cavity method rigorously Current trends in variational Bayes inference Message passing algorithms Applications on machine learning in condensed matter physics Information processing in biological systems

    Deadline Registration : February 28, 2018


    Gerard Ben Arous (Courant Institute) Giulio Biroli (CEA Saclay, France) Nicolas Brunel (Duke University) Yann LeCun (Courant Institute and Facebook) Michael Jordan (UC Berkeley) Stephane Mallat (ENS et college de France) Andrea Montanari (Stanford) Dmitry Panchenko (University of Toronto, Canada) Sundeep Rangan (New York University) Riccardo Zecchina (Politecnico Turin, Italy)


    Antonio C Auffinger (Northwestern University, USA) Afonso Bandeira (Courant Institute, USA) Jean Barbier (Queens Mary, UK) Quentin Berthet (Cambridge UK) Jean-Philippe Bouchaud (CFM Paris, France) Silvio Franz (Paris-Orsay, France) Surya Ganguli (Stanford, USA) Alice Guionnet (ENS Lyon, France) Aukosh Jagganath (Harvard, USA) Yoshiyuki Kabashima (Tokyo Tech, Japan) Christina Lee (Microsoft Research, USA) Marc Lelarge (ENS Paris, France) Marc Mezard (ENS Paris, France) Leo Miolane (ENS Paris, France) Remi Monasson (ENS Paris, France) Giorgio Parisi (Roma La Sapienza, Italy) Will Perkins (University of Birmingham, UK) Federico Ricci-Tersenghi (Roma La Sapienza, Italy) Cythia Rush (Columbia, USA) Levent Sagun (CEA Saclay, France) Samuel S. Schoenholz (Google Brain, USA) David Jason Schwab (CUNY, USA) Guilhem Semerjian (ENS Paris, France) Alexandre Tkatchenko (University of Luxembourg) Naftali Tishby (Hebrew University, Israel) Pierfrancesco Urbani (CEA Saclay, France) Francesco Zamponi (ENS Paris, France)

    Organizing Committee

    Florent Krzakala (ENS & UPMC, Paris) Lenka Zdeborova (CEA & CNRS, Saclay)

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  • 35th International Conference on Machine Learning (ICML 2018) Stockholm, Sweden July 10 -15 2018


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    This ICML workshop seeks to broaden the role of geometric models and thinking in machine-learning research.

    The interplay between machine learning and geometry is an active field of research drawing the attention of researchers from many fields as it offers not only beautiful mathematical and statistical theory but also substantial impact on important real-world problems in machine learning. Examples of this interplay include, but are not limited to:

    geometric insights into deep learning (helping us to better understand and improve these models); statistical models that respect and exploit the constraints of geometric data ("statistics on manifolds"); (Riemannian) manifold learning; viewing probability distributions as points on a nonlinear manifold ("information geometry"); optimization over nonlinear manifolds; Wasserstein spaces and their applications; understanding the effect of encoding group invariances (e.g. rotation invariance) on the intrinsic geometry of the data space, which becomes a quotient space.

    This ICML workshop is co-located with ICML, IJCAI and AAMAS in Stockholm.

    The Bosch center for AI generously sponsors a prize for the best abstract, which will be presented at the workshop.

    Confirmed Speakers

    Justin Solomon Assistant Professor, MIT Nina Miolane Research Fellow, Stanford University David Rosen MIT LIDS, formerly at Oculus Research John Skilling Research Director, Maximum Entropy Data Consultants Ltd. Frank Nielsen Sony Computer Science Laboratories Inc, Japan Stefano Soatto Professor, UCLA


    The workshop takes place at the ICML conference venue, room A4.


    The conference is jointly organized by:

    Søren Hauberg, Technical University of Denmark Aasa Feragen, University of Copenhagen Oren Freifeld, Ben-Gurion University of the Negev Nicolas Boumal, Princeton University Michael Schober, Bosch Center for Artificial Intelligence

    For matters regarding the conference, you can contact the organizers at gimli.meeting[at]

    We are grateful for funding from the Bosch Center for Artificial Intelligence, the Villum Fonden Young Investigator program and the European Research Council (ERC) through a starting grant.

  • Oct 9 2018 - Academie des Sciences - Paris - France


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    Conférence-débat de l'Académie des sciences, de 14h30 à 16h45, dans la Grande salle des séances de l’Institut de France - Inscription obligatoire avant le 9 octobre 2018

    Alain Berthoz, professeur honoraire au Collège de France, membre de l'Académie des sciences et de l’Académie des technologies.


    Neil Burgess, professeur et directeur de l’Institute of Cognitive Neuroscience, University College London, membre de la Royal Society et de l’Academy of Medical Sciences Laure Rondi-Reig, directrice de recherche au CNRS à l’Institut de Biologie Paris–Seine, Université Pierre-et-Marie-Curie Alain Berthoz, professeur honoraire au Collège de France, membre de l'Académie des sciences et de l’Académie des technologies Daniel Bennequin, professeur émérite au département de mathématiques de l’université Paris Diderot.

    Le thème général de cette séance sera d’exposer des découvertes et théories récentes concernant le traitement cérébral de l’espace et plus particulièrement la flexibilité des stratégies cognitives et la diversité des réseaux de traitement des divers espaces d’action.
    Neil Burgess décrira les divers types de neurones de la formation hippocampique qui codent la place, la direction, les bords, leur organisation en grilles et les relations avec les objets. Il décrira un modèle mathématique de transformation entre les divers référentiels spatiaux (égocentré, allocentré etc.) et son utilisation pour prédire des symptômes de déficits de la mémoire spatiale dans des pathologies post-traumatiques.
    Laure Rondi-Reig décrira les réseaux neuronaux impliqués dans des activités de navigation spatiale, l’apprentissage et les troubles de la mémoire, dans le cas du vieillissement par exemple. Des données expérimentales sur les mécanismes neurophysiologiques et la diversité des réseaux corticaux et sous corticaux de la cognition spatiale et des modèles inspirés des neurosciences computationnelles seront présentés.
    Alain Berthoz proposera que le cerveau traite avec des réseaux différents et des géométries différentes pour les divers espaces (corps, préhension, locomoteur, environnemental). Il décrira des données récentes sur les bases neurales des changements de perspective, le développement chez l’enfant des fonctions visuo-spatiales et l’intervention de la manipulation des référentiels spatiaux dans l’empathie et son implication en pathologie psychiatrique.
    Daniel Bennequin proposera que pour guider l’adaptation des actions et des perceptions, les cerveaux des animaux et de l’homme mettent en place une variété de géométries, Euclidiennes et non Euclidiennes, et de dynamiques. L’exposé présentera un travail sur une nouvelle sorte de géométrie : un topos d’espaces au-dessus d’une catégorie (site) représentant la préparation et l’exécution d’une classe de mouvements; par exemple la préhension, la locomotion, la navigation, l’imagination.

  • 3-5 oct. 2018, Institute Camille Jordan, Saint-Étienne, France


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    October 3rd to October 5th 2018
    Workshop at the Institute Camille Jordan
    Faculté des Sciences et Techniques
    Bâtiment A, salle A 14

    Aim of the workshop

    The aim of the workshop is to bring together researchers at the junction of discrete random structures, in particular random graphs, random walks, and its applications to complex networks.

    Poster of the workshop

    Organizing committee

    Dieter Mitsche Pascal Grail Pascale Villet


    Julien Barré (Univ. d'Orléans): Rigidity percolation and random graphs
    Oriane Blondel (CNRS, Univ. Lyon 1)
    Jérémie Bouttier (ENS Lyon): Some aspects of random maps coupled with matter systems
    Pierre Calka (Univ. Rouen)
    Philippe Chassaing (Univ. de Lorraine): The impatient collector
    David Coupier (Univ. Valenciennes)
    Josep Diaz (UPC Barcelona): Evolutionary Graph Theory
    Roland Diel (Univ. Nice Côte d'Azur)
    Joachim Giesen (Univ. Jena)
    Emmanuel Jacob (ENS Lyon)
    Lefteris Kirousis (National and Kapodistrian Univ. Athens): The Lovasz Local Lemma: An introduction and some recent results
    Antoine Lejay (INRIA Nancy Grand-Est)
    Grégory Miermont (ENS Lyon)
    Grigory Panasenko (Univ. Saint-Etienne)
    Guillem Perarnau (Univ. Birmingham): Efficient sampling of random colorings
    Vlady Ravelomanana (Univ. Paris 7)
    Christophe Sabot (Univ. Lyon 1)
    Bruno Schapira (Univ. Aix-Marseille)
    Fabio Toninelli (CNRS, Univ. Lyon 1)


    Details will be announced soon.
    Talks start wednesday October 3 after lunch and end Friday October 5 before lunch.


    No fee. However, for logistical reasons, registration is mandatory.
    In order to register, please contact us by email: dieter.mitsche(AT), by September 26.

    Practical Information

    The talks will take place in the Faculty of Sciences, room A 14, 23 rue du Docteur Paul Michelon, Saint-Étienne.

    Venue: Address: Institut Camille Jordan, Faculté des Sciences et Techniques, 23 rue du Docteur Paul Michelon, 42023 Saint-Étienne Cedex 2

    How to arrive from Aeroport Lyon Saint-Exupery: The shuttle Ouibus makes the connection form Lyon Saint-Exupery airport to Saint-Etienne main train station.
    Alternative: By Rhône-express ( + train (TER Lyon Part Dieu-Saint-Etienne: (

    How to arrive from Lyon-Part Dieu or Lyon-Perrache: By train, (TER journeys to/from Saint-Etienne and Lyon take under 50 minutes).

    How to go from the main station Saint-Etienne Chateaucreux to the Institute Camille Jordan: The city bus company: STAS:
    The Bus line M4 takes you from the main train station (Saint-Etienne Châteaucreux) to the faculty of Sciences.

    Continental Hôtel ★★
    10 Rue François Gillet - 42000 Saint-Étienne

    Hôtel du Cheval Noir ★★★
    11 Rue François Gillet - 42000 Saint-Étienne


    Access and Campus Maps

  • Chaire d'excellence IHP - Oct 8th - November 26th 2018 IHP, Paris


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    An overview over recently developed methods for proving decay to equilibrium for dissipative dynamical systems is presented. The methodology is based on Lyapunov functionals, often with the physical interpretation of a (generalized) entropy or free energy.
    The course features a short formal introduction to stochastic processes, aimed at an audience with a PDE background. The concepts of martingales and time reversal of homogeneous Markov processes are used to derive local decay results for relative
    entropies. These are applied to various examples of Levy processes, including applications in kinetic transport theory, in mathematical biology, and in chemical reaction networks. Quantitative decay results are derived from entropyentropy decay inequalities or from inequalities between entropy decay and its time derivative, i.e. by the celebrated Bakry-Emery approach.
    A focus is on hypocoercive problems, where decay to equilibrium holds despite the fact that the decay term for the natural entropy functionals is only semi-denite. Various recent approaches to such problems, mainly in kinetic theory, are compared and unied.
    Finally, examples of nonlinear problems are discussed, and the question of structural assumptions allowing for entropy decay is examined.

    Lundi 8 octobre 2018
    Lundi 15 octobre 2018
    Lundi 22 octobre 2018
    Lundi 29 octobre 2018
    Lundi 5 novembre 2018
    Lundi 12 novembre 2018
    Mercredi 21 novembre 2018
    Lundi 26 novembre 2018
    De 14h à 17h
    Institut Henri Poincaré - Salle 314*

    *et exceptionnellement salle 201 le 29/10/18
    11 rue Pierre et Marie 75005 Paris

  • Conference 27-31 Mai 2019, Paris-Diderot Université


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    In May 1969 the groundbreaking book of Jean-Marie Souriau appeared, Structure des Systèmes Dynamiques. We will celebrate, in 2019, the jubilee of its publication, with a conference in honour of the work of this great scientist.
    Welcome to the conference !


    Paris-Diderot Université
    4 Rue Elsa Morante, 75013 Paris


    Just send us an email with your name and your affiliation (if any)…


    Frédéric Barbaresco (Thales Group, France) Daniel Bennequin (Université Paris Diderot, France) Jean-Pierre Bourguignon (European Research Council, Europe) Pierre Cartier (IHES Paris, France) Maurice Courbage (Université Paris Diderot, France) Dan Christensen (University of Western Ontario, Canada) Thibault Damour (IHES Paris, France) Paul Donato (Aix-Marseille Université, France) Souriau, a synthesis of his work Paolo Giordano (Wolfgang Pauli Institute, Wien, Austria) Serap Gürer (Galatasaray Üniversitesi, Turkey) Differential Forms on Stratified Spaces Patrick Iglesias-Zemmour (Aix-Marseille Université, France) Symplectic Diffeology. Dissipative Thermodynamics in General Relativity Yael Karshon (University of Toronto, Canada) Yvette Kosmann-Schwarzbach (Paris, France) Marc Lachieze-Rey (APC — Université Paris Diderot, France) Martin Pinsonnault (University of Western Ontario, Canada) Elisa Prato (Università degli Studi di Firenze, Italy) Urs Schreiber (Czech Academy of the Sciences, Czech Republic) Jedrzej Sniatycki (University of Calgary, Canada) Roland Triay (Aix-Marseille Université, France) San Vũ Ngọc (Université de Rennes, France) Jordan Watts (Central Michigan University, USA) Alan Weinstein (University of California, Berkeley, USA) Enxin Wu (汕头大学Shantou University, China)


    If you want to present a poster, just send us an email with your name, affiliation, the title of your poster and possibly a link on a pdf.


    In May 1969 the groundbreaking book of Jean-Marie Souriau appeared, Structure des systèmes dynamiques. We will celebrate, in 2019, the jubilee of its publication, with a conference in honour of the work of this great scientist.

    The influence of Souriau’s work is felt in the areas he has innovated or developed in his own way. It is important to take stock of it, in particular in order to make the current and future generations aware of his original and deep thought.

    The main reasons for organising this conference are the singularity, and at the same time the scope, of the work of Souriau. He was able to create, in his time, a homogeneous group of “Souristes” who for the most part have reached maturity and are able today to convey the originality of this work. It is also time to take stock of the important work to which it has given rise among foreign researchers, many of whom we will invite to speak. The work of Jean-Marie Souriau lives on in different areas of the scientific world and, at different levels of depth, in the development of mathematics and physics, and therefore according to the different temporalities of the history of these disciplines.

    All scholars, old and new, recognize this. Souriau’s work is particularly important work for the relationships he has established and developed between physics and geometry. He is one of the most important founders of symplectic geometry, and the theoretical exploitation of his work in this field is far from exhausted.

    André Lichnerowicz has said that his work could belong to four international scientific unions: mathematics, mechanics, physics and astronomy. But what interests us is not only Souriau the scientist, but also the philosopher. What is striking is the unity of his thought through the variety of his fields of interest. It is likely true that this thought grows deeper as its areas of application expand. The object of our inquiry will be to analyse his thought insofar as it is related to the philosophy of science and even to pure philosophy.

    This conference aims to review the entire work of Jean-Marie Souriau in the five areas in which he worked.

    1. Symplectic mechanics. Symplectic structure of the space of the movements of a dynamic system, action of invariance groups, moment map, symplectic cohomology, barycentric decomposition theorem. Elementary systems (particles): homogeneous symplectic variety, classification by coadjoint orbits.

    2. Geometric Quantization. Prequantization condition, pre-quantum bundle, polarizations, quantification of coadjoint orbits.

    3. Thermodynamics. Geometric statistical equilibria in symplectic manifolds. Vector temperature and thermodynamic dissipative model in general relativity.

    4. General Relativity and Cosmology. General Covariance Principle, Robertson-Walker universe model with cosmological constant.

    5. Diffeology. Renewal of the formal framework of differential geometry by a stable space category by all natural set operations (i.e. complete, complete, Cartesian closed). This includes highly singular spaces that may not even be separated, infinite dimensional spaces, and so on.

    6. Philosophy of Science, Epistemology, History of Science. History of each of the domains evoked (symplectic mechanics, quantification, cosmology and relativity, thermodynamics). In each of these areas Souriau introduced new ideas. These new views have not ceased to be relevant and fruitful. The task of a philosophy of science will be to highlight this novelty.

    As far as philosophy is concerned, a new question has been raised about the importance of this work, in its variety and unity, and in its impact on the philosophy of science and philosophy in general, which we propose to deal with in this conference. Souriau’s originality manifested itself in his will and in his attempts to create new languages. We will analyze, in the philosophy section, this important aspect of his work, most evident in his work in geometry and relativity.

    The Organizers: J.-J. Szczeciniarz & P. Iglesias-Zemmour


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  • Geo-Sci-Info

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    The workshop will bring together experts in geometric mechanics and optimal transport, with emphasis on stochastic aspects. The goal is to explore parallel connections between the two fields such as, for example, the Schrödinger problem and the Monge-Kantorovich theory.


    Alexis Arnaudon (Imperial College)

    Marc Arnaudon (Univ. Bordeaux)

    Yann Brenier (École polytechnique Paris)

    Giovanni Conforti (École polytechnique Paris)

    Shizan Fang (Univ. Dijon)

    François Gay-Balmaz (ENS Paris)

    Ivan Gentil (Univ. Lyon)

    Rémi Lassalle (Univ. Paris Dauphine)

    Christian Léonard (Univ. Paris Nanterre)

    Luca Nenna (Univ. Paris Sud)

    Gabriel Peyré (ENS Paris)

    Nicolas Privault (NTU Singapore)

    Tudor Ratiu (Univ. Shanghai Jiao Tong & EPFL)

    Luigia Ripani (Univ. Lyon)

    Sylvie Roelly (Inst. Math. Potsdam)

    Esmeralda Sousa Dias (IST Univ. Lisboa)

    Luca Tamanini (SISSA Trieste)

    François-Xavier Vialard (Univ. Paris Dauphine)

    Pierre Vuillermot (Univ. Lisboa & IECL Nancy)

    to be confirmed

    Aims and scope:

    Organizing committee: A.B. Cruzeiro, L. Monsaingeon, J.-C. Zambrini

    Contact: L. Monsaingeon

    Dowload the poster of the conference here!

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  • Institut Henri Poincré - Nov. 17th 2018




    Samedi 17 novembre 2018
    Amphi Hermite, Institut Henri Poincaré

    Kirone Mallick - Thermodynamique et information • 10h Olivier Rioul - La théorie de l’information sans peine • 11h Sergio Ciliberto - Landauer et le démon de Maxwell • 14h Elham Kashefi - Quantum Verification • 15h Christophe Salomon - La simulation quantique • 16h

  • 30 juin - 6 juillet 2019 - École d’Été - Peyresq, France


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    Le GRETSI et le GdR ISIS organisent depuis 2006 une École d'Été annuelle en traitement du signal et des images. Ouverte à toute personne intéressée (académique ou industrielle), elle s'adresse prioritairement à des doctorants ou chercheurs en début de carrière, et a pour but de présenter une synthèse ainsi que les avancées les plus récentes dans un thème de recherche d'actualité. Cette École d'Été a lieu tous les ans et a pour cadre le magnifique village de Peyresq, perché à 1500 mètres d'altitude sur un éperon rocheux des Alpes de Haute Provence (

    Thème de l'école 2019

    La session 2019 a pour thème :

    Géométrie de l'information pour le traitement du signal et des images

    La géométrie de l’information est un thème qui a généré une activité croissante dans la communauté signal et images. Parmi les thématiques incluses dans la géométrie de l’information, on compte par exemple : la définition de distances ou divergences sur des espaces courbes et les applications en classification, les statistiques sur les groupes et les variétés avec des applications en tracking, filtrage et estimation; ainsi que la caractérisation des performances des estimateurs de matrices de covariance. Le nombre des applications est croissant et les potentialités importantes, mais il faut constater que les méthodologies et concepts impliqués en géométrie de l’information ne font pas partie de beaucoup de cursus. Cette École d’Été envisage de proposer aux doctorants (en priorité) une introduction aux concepts de la théorie (géométrie différentielle) ainsi qu’un état des lieux de leurs applications en traitement des signaux et des images.
    L'École comporte à la fois des cours tutoriaux et des sessions ouvertes permettant aux participants de présenter leurs travaux et de confronter leurs idées.
    Vous pouvez télécharger l'affiche et diffuser l'information autour de vous.

    L'École comportera à la fois des cours tutoriaux, ainsi que des sessions ouvertes permettant aux participants de présenter leurs travaux et de confronter leurs idées.
    L'emploi du temps détaillé de l'École sera prochainement disponible.
    Programme prévisionnel

    1. Introduction aux outils de géométrie différentielle et optimisation en traitement des données (5h)
    Conférencier : P.A. Absil (University of Louvain, Belgium)

    2. Géométrie de l’information et ses applications (5h)
    Conférencier : F. Nielsen (Sony Computer Science Laboratories Inc & Ecole Polytechnique)

    3. Statistiques géométriques et leurs applications aux formes anatomiques (5h)
    Conférencier : X. Pennec (INRIA Sophia Antipolis)

    4. Estimation récursive sur les variétés Riemanniennes (2h)
    Conférencier : S. Said (Université de Bordeaux)

    5. Bornes de Cramér-Rao intrinsèques et matrices de covariance (2h)
    Conférencier : A. Renaux (Université Paris Saclay)

    6. Les structures élémentaires de la géométrie de l'information et la métrique de Fisher-Koszul-Souriau : exemples d'applications pour le signal radar (2h)
    Conférencier : F. Barbaresco (Thales)

    Les demandes d'inscriptions à l'École d'Été seront ouvertes à partir de la fin Novembre 2018.
    L'École d'Été est ouverte à toute personne intéressée, académique ou industrielle. Le nombre de participants étant toutefois limité par la capacité d'accueil du lieu, une priorité sera donnée aux doctorants, aux chercheurs en début de carrière et aux industriels partenaires du GdR ISIS.
    Une participation financière couvrant les frais d'hébergement et de restauration est demandée.
    La participation aux frais est de 350€ pour les doctorants et de 600€ pour les autres (chercheurs titulaires, ingénieurs, post-doctorants, industriels).

    Dates importantes

    novembre 2018: Ouverture du service d'enregistrement des demandes d'inscription. 18 février 2019: Clôture du service d'enregistrement des demandes d'inscription. 19 mars 2019: Notification des inscriptions. Ouverture du service des inscriptions définitives. 3 mai 2019: Fermeture du service des inscriptions définitives. 30 juin - 6 juillet 2019: École d'Eté.

    Comite d'Organisation

    Patrick Flandrin Directeur de Recherche CNRS, Laboratoire de Physique, ENS de Lyon. Cédric Richard Professeur des Universités, Laboratoire Lagrange, Université de Nice.

    Direction Scientifique

    Guillaume Ginolhac Professeur des Universités, LISTIC, Université Savoie Mont-Blanc. Nicolas Le Bihan Directeur de Recherche CNRS, Gipsa-Lab, UGA, Grenoble INP.

    Pour toute demande de renseignement, veuillez nous envoyer un mèl à
    peyresq19_l AT

    Quand arriver à Peyresq (et en repartir) ?
    Pour des raisons pratiques d'organisation, l'arrivée à Peyresq devra se faire impérativement dans l'après-midi ou le début de soirée du dimanche 30 juin (pas avant pour cause d'occupation du site, et pas après car les cours commenceront le lundi matin à 9h00).
    Comment accéder à Peyresq (et en repartir) ?
    1- Par bus Un bus gratuit sera mis à la disposition des participants pour effectuer directement les trajets entre Nice et Peyresq (2 heures environ). Aller : départ de la gare SNCF de Nice à 17h puis du terminal 2 de l'aéroport de Nice le dimanche 30 juin à 17h30.
    Retour : départ de Peyresq le samedi 6 juillet à 9h00, passage au terminal 2 de l'aéroport de Nice à 11h30 puis en gare SNCF à 12h.
    2- Par le train: Se rendre à la "Gare du Sud (des Chemins de Fer de Provence)", 4 rue Alfred Binet, située à 15 minutes environ de la Gare principale de Nice Prendre le train des Pignes" (qui mène à Digne) et descendre à Annot. Les horaires sont consultables sur le site des Chemins de Fer de Provence. Il est à noter que la liaison entre Annot et Peyresq (20 km) nécessite alors un taxi, qu'il est prudent de réserver (
    3- Par la route : Coordonnées GPS : (N 44° 04' 02" - E 06° 37' 04") Attention : une fois arrivé à Peyresq, il est impératif de se garer à l'extérieur du village.

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  • February 7th 2019 - Ecole des Mines ParisTech - France


    Journée ISS France

    La 42ème édition de la journée ISS France aura lieu le Jeudi 07 Février 2019

    Ecole des Mines ParisTech 60, boulevard Saint-Michel 75272 Paris cedex 06


    Corinne Lagorre, Université Paris Est Créteil, LISSI, 61 avenue du Gal de Gaulle, 94000 Créteil Bruno Figliuzzi, Centre de Morphologie Mathématique, 35 rue Saint Honoré, 77305 Fontainebleau

    La participation est gratuite mais l'inscription est obligatoire. Vous pouvez vous inscrire ou proposer une communication par par mail à l'adresse:


    Les journées d’étude de l’ISS France (International Society for Stereology) rassemblent chaque année des acteurs de l’analyse des images numériques, de la stéréologie et de leurs applications et connexions. La volonté a été affirmée depuis de nombreuses années de faire des journées d’étude de l’ISS France un lieu de rencontre, d’échange et d’expérimentation réellement pluridisciplinaire, qui puise ses forces dans l’ensemble du patrimoine intellectuel actuel. Le point d’ancrage reste délibérément l’image, et les techniques, sciences, applications et arts qui s’y intéressent.

    Vous trouverez sur cette page les programmes des deux dernières journées d’étude ; vous pourrez en particulier y découvrir les thématiques classiquement abordées:

    La Session Méthodes est principalement axée sur les techniques issues de la morphologie mathématique, Les sessions Applications parcourent les principaux travaux réalisés dans les domaines des Biosciences, des Sciences des Matériaux ou de la Géographie Mathématique, notamment.
  • Geo-Sci-Info

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    Shape Analysis, Stochastic Mechanics and Optimal Transport

    Boris Khesin, University of Toronto: Beyond Arnold’s geodesic framework of an ideal hydrodynamics
    We discuss a ramification of Arnold’s group-theoretic approach to ideal hydrodynamics as the geodesic flow for a right-invariant metric on the group of volume-preserving diffeomorphisms. We show such problems of mathematical physics as the motion of vortex sheets or fluids with moving boundary, have Lie groupoid, rather than Lie group, symmetries, and describe the corresponding geometry and equations. (This is a joint work with Anton Izosimov.)
    Watch video | Download video Gerard Misiolek, University of Notre Dame: The L2 exponential map in 2D and 3D hydrodynamics
    In the 1960's V. Arnold showed how solutions of the incompressible Euler equations can be viewed as geodesics on the group of diffeomorphisms of the fluid domain equipped with a metric given by fluid's kinetic energy. The study of the exponential map of this metric is of particular interest and I will describe recent results concerning its properties as well as some necessary background.
    Watch video | Download video Klas Modin, Chalmers University of Technology / University of Gothenburg: Semi-invariant metrics on diffeos
    We investigate a generalization of cubic splines to Riemannian manifolds. Spline curves are defined as minimizers of the spline energy---a combination of the Riemannian path energy and the time integral of the squared covariant derivative of the path velocity---under suitable interpolation conditions. A variational time discretization for the spline energy leads to a constrained optimization problem over discrete paths on the manifold. Existence of continuous and discrete spline curves is established using the direct method in the calculus of variations. Furthermore, the convergence of discrete spline paths to a continuous spline curve follows from the Γ-convergence of the discrete to the continuous spline energy. Finally, selected example settings are discussed, including splines on embedded finite-dimensional manifolds, on a high-dimensional manifold of discrete shells with applications in surface processing, and on the infinite-dimensional shape manifold of viscous rods.
    Watch video | Download video Ana Cruzeiro, University of Lisbon : On some relations between Optimal Transport and Stochastic Geometric Mechanics
    We formulate the so-called Schrodinger problem in Optimal Transport on lie group and derive the corresponding Euler-Poincaré equations.
    Watch video | Download video | PDF presentation Christian Léonard, Universite Paris Nanterre: Some ideas and results about gradient flows and large deviations
    In several situations, the empirical measure of a large number of random particles evolving in a heat bath is an approximation of the solution of a dissipative PDE. The evaluation of the probabilities of large deviations of this empirical measure suggests a way of defining a natural ``large deviation cost'' for these fluctuations, very much in the spirit of optimal transport. Some standard Wasserstein gradient flow evolutions are revisited in this perspective, both in terms of heuristic results and a few rigorous ones. This talk gathers several joint works with Julio Backhoff, Giovanni Conforti, Ivan Gentil, Luigia Ripani and Johannes Zimmer.
    Watch video | Download video | PDF presentation Marc Arnaudon, Université de Bordeaux: A duality formula and a particle Gibbs sampler for continuous time Feynman-Kac measures on path spaces
    "Continuous time Feynman-Kac measures on path spaces are central in applied probability, partial differential equation theory, as well as in quantum physics. I will present a new duality formula between normalized Feynman-Kac distribution and their mean field particle interpretations. Among others, this formula will allow to design a reversible particle Gibbs-Glauber sampler for continuous time Feynman-Kac integration on path spaces. This result extends the particle Gibbs samplers introduced by Andrieu-Doucet-Holenstein in the context of discrete generation models to continuous time Feynman-Kac models and their interacting jump particle interpretations. I will also provide new propagation of chaos estimates for continuous time genealogical tree based particle models with respect to the time horizon and the size of the systems. These results allow to obtain sharp quantitative estimates of the convergence rate to equilibrium of particle Gibbs-Glauber samplers. "
    Watch video | Download video | PDF presentation Alexis Arnaudon, Imperial College London : Geometric modelling of uncertainties
    In mechanics, and in particular in shape analysis, taking into account the underlying geometric properties of a problem to model it is often crucial to understand and solve it. This approach has mostly been applied for isolated systems, or for systems interacting with a well-defined, deterministic environment. In this talk, I want to discuss how to go beyond this deterministic description of isolated systems to include random interactions with an environment, while retaining as much as possible the geometric properties of the isolated systems. I will discuss examples from geometric mechanics to shape analysis, ranging from interacting rigid bodies with a heath bath to uncertainties quantification in computational anatomy.
    Watch video | Download video Bernhard Schmitzer, University of Münster: Semi-discrete unbalanced optimal transport and quantization
    "Semi-discrete optimal transport between a discrete source and a continuous target has intriguing geometric properties and applications in modelling and numerical methods. Unbalanced transport, which allows the comparison of measures with unequal mass, has recently been studied in great detail by various authors. In this talk we consider the combination of both concepts. The tessellation structure of semi-discrete transport survives and there is an interplay between the length scales of the discrete source and unbalanced transport which leads to qualitatively new regimes in the crystallization limit."
    Watch video | Download video | PDF presentation Carola-Bibiane Schönlieb, University of Cambridge : Wasserstein for learning image regularisers
    In this talk we will discuss the use of a Wasserstein loss function for learning regularisers in an adversarial manner. This talk is based on joint work with Sebastian Lunz and Ozan Öktem, see
    Watch video | Download video | PDF presentation Tryphon Georgiou, University of California, Irvine : Interpolation of Gaussian mixture models and other directions in Optimal Mass Transport
    Watch video | Download video Laurent Younes, John Hopkins University : Normal coordinates and equivolumic layers estimation in the cortex (tentative)
    Watch video | Download video Barbara Gris, Université Pierre-et-Marie-Curie: Analyze shape variability via deformations
    I will present how shape registration via constrained deformations can help understanding the variability within a population of shapes.
    Watch video | Download video Dongyang Kuang, University of Ottawa : Convnets, a different view of approximating diffeomorphisms in medical image registration
    As with the heat of artificial intelligence, there are more and more researches starting to investigate the possible geometric transformations using data-driven methods such as convolutional neural networks. In this talk, I will start by introducing some existing work that learn 2D linear transformations in an unsupervised way. This then will be followed by an overview of some recent works focusing on nonlinear transformations in 3D volumetric data. Finally, I will present results from the joint work with my supervisor using our network architecture called FAIM.
    Watch video | Download video Stephen Preston, Brooklyn College : Solar models for Euler-Arnold equations
    Many one-dimensional Euler-Arnold equations can be recast in the form of a central-force problem Γtt(t,x)=−F(t,x)Γ(t,x), where Γ is a vector in ℝ2 and F is a nonlocal function possibly depending on Γ and Γt. Angular momentum of this system is precisely the conserved momentum for the Euler-Arnold equation. In particular this picture works for the Camassa-Holm equation, the Hunter-Saxton equation, and the Okamoto-Sakajo-Wunsch family of equations. In the solar model, breakdown comes from a particle hitting the origin in finite time, which is only possible with zero angular momentum. Results due to McKean (for Camassa-Holm), Lenells (for Hunter-Saxton), and Bauer-Kolev-Preston/Washabaugh (for the Wunsch equation) show that breakdown of smooth solutions occurs exactly when momentum changes from positive to negative. I will discuss some conjectures and numerical evidence for the generalization of this picture to other equations such as the μ-Camassa-Holm equation or the DeGregorio equation.
    Watch video | Download video Cy Maor, University of Toronto : Vanishing geodesic distance for right-invariant Sobolev metrics on diffeomorphism groups
    Since the seminal work of Arnold on the Euler equations, many important PDEs were shown to be geodesic equations of diffeomorphism groups of manifolds, with respect to various Sobolev norms. But what about the geodesic distance induced by these norms? Is it positive between different diffeomorphisms, or not? In this talk I will show that the geodesic distance on the diffeomorphism group of an n-dimensional manifold, induced by the Ws,p norm, does not vanish if and only if s≥1 or sp>n. The first condition detects changes of volume, while the second one detects transport of arbitrary small sets. I will focus on the case where both conditions fail, and how this enables the construction of arbitrary short paths between diffeomorphisms. Based on a joint work with Robert Jerrard, following works of Michor-Mumford, Bauer-Bruveris-Harms-Michor and Bauer-Harms-Preston.
    Watch video | Download video Philipp Harms, University of Freiburg : Smooth perturbations of the functional calculus and applications to Riemannian geometry on spaces of metrics.
    We show that the functional calculus, which maps operators A to functionals f(A), is holomorphic for a certain class of operators A and holomorphic functions f. Using this result we are able to prove that fractional Laplacians depend real analytically on the underlying Riemannian metric in suitable Sobolev topologies. As an application we obtain local well-posedness of the geodesic equation for fractional Sobolev metrics on the space of all Riemannian metrics. (Joint work with Martins Bruveris, Martin Bauer, and Peter W. Michor).
    Watch video | Download video Eric Klassen, Florida State University : Comparing Shapes of Curves, Surfaces, and Higher Dimensional Immersions in Euclidean Space.
    Comparing shapes and treating them as data for statistical analyses has many applications in biology and elsewhere. Certain elastic metrics on spaces of immersions have proved very effective for comparing curves and surfaces. The elastic metrics which have proved most useful for computation have been first order metrics, i.e., they compare tangent vectors on the shapes rather than points on the shapes. In this talk I will present a unifying view of these metrics, shedding new light on old methods and, I hope, suggesting new methods for analyzing surfaces and higher dimensional shapes.
    Watch video | Download video Facundo Memoli, The Ohio State University : Metrics on the collection of dynamic shapes.
    When studying flocking/swarming behaviors in animals one is interested in quantifying and comparing the dynamics of the clustering induced by the coalescence and disbanding of groups of animals. In a similar vein, when attempting to classify motion capture data according to action one is confronted with having to match/compare shapes that evolve with time. Motivated by these applications, we study the question of suitably metrizing the collection of all dynamic metric spaces (DMSs). We construct a suitable metric on this collection and prove the stability of several natural invariants of DMSs under this metric. In particular, we prove that certain zigzag persistent homology invariants related to dynamic clustering are stable w.r.t. this distance. These lower bounds permit the efficient classification of dynamic shape data in applications. We will show computational experiments on dynamic data generated via distributed behavioral models. This is joint work with Woojin Kim and Zane Smith
    Watch video | Download video Tom Needham, Ohio State University : Gromov-Monge Quasimetrics and Distance Distributions.
    In applications in computer graphics and computational anatomy, one seeks a measure-preserving map from one shape to another which preserves geometry as much as possible. Inspired by this, we consider a notion of distance between arbitrary compact metric measure spaces by blending the Monge formulation of optimal transport with the Gromov-Hausdorff construction. We show that the resulting distance is an extended quasi-metric on the space of compact mm-spaces. This distance has convenient lower bounds defined in terms of distance distributions; these are functions associated to mm-spaces which have been used frequently as summaries in data and shape analysis applications. We provide rigorous results on the effectiveness of these lower bounds when restricted to simple classes of mm-spaces such as metric graphs or plane curves.This is joint work with Facundo Mémoli.
    Watch video | Download video Jean-David Benamou, INRIA Rocquencourt : Dynamic formulations of optimal transportation and variational relaxation of Euler equations.
    We will briefly recall the classical Optimal Transportation Framework and its Dynamic relaxations. We will show the link between these Dynamic formulation and the so-called MultiMarginal extension of Optimal Transportation. We will then describe the so-called Iterative Proportional Fitting Procedure (aka Sinkhorn method) which can be efficiently applied to the multi-marginal OT setting. Finally we will show how this can be used to compute generalized Euler geodesics due to Brenier. This problem can be considered as the oldest instance of Multi-Marginal Optimal Transportation problem. Joint work with Guillaume Carlier (Ceremade, Universite Paris Dauphine, France) and Luca Nenna (U. Paris Sud, France).
    Watch video | Download video Tudor Ratiu, Shanghai Jiao Tong University: Group valued momentum maps
    Watch video | Download video Andrea Natale, Inria : Generalized H(div) geodesics and solutions of the Camassa-Holm equation
    Watch video | Download video Jean Feydy, Ecole Normale Supérieure : Robust shape matching with optimal transport
    Watch video | Download video | PDF presentation Alice Le Brigant*, ENAC - Ecole Nationale de l'Aviation Civile : Quantization on a Riemannian manifold with application to air traffic control
    Watch video | Download video

    Capture du 2018-12-25 17-04-18.png

  • The 18th International Conference, Graduate School of Mathematics, Nagoya University - March 27–29, 2019


    The 18th International Conference, Graduate School of Mathematics, Nagoya University
    Information Geometry and Affine Differential Geometry III


    March 27–29, 2019

    Rm.~509, Mathematics Bldg., Nagoya University


    Shun-ichi Amari (Riken), Frédéric Barbaresco (Thales Land & Air Systems), Michel Nguiffo Boyom (Université de Montpellier), Shinto Eguchi (Institute of Statistical Mathematics), Hitoshi, Furuhata (Hokkaido University), Hiroto Inoue (Kyushu University), Hideyuki Ishi (Nagoya University), Amor Keziou (Université de Reims Champagne-Ardenne), Yongdo Lim (Sungkyunkwan University), Hiroshi Matsuzoe (Nagoya Institute of Technology), Atsumi Ohara (Fukui University), Philippe Regnault (Université de Reims Champagne-Ardenne), Tatsuo Suzuki (Shibaura Institute of Technology), Jun Zhang (University of Michigan)

    Organizing Committee

    Hideyuki Ishi (Nagoya University), Hiroshi Matsuzoe (Nagoya Institute of Technology), Atsumi Ohara (Fukui University), Jun Zhang (University of Michigan)

    Contact to
    Hideyuki Ishi (hideyuki (at)

  • part of the conference Prague Stochastics 2019 - August 19–23, 2019 -UTIA, the Institute of Information Theory and Automation of the Academy of Sciences of the Czech Republic, Prague.


    Workshop in memory of František Matúš

    August 19–23, 2019 -UTIA, the Institute of Information Theory and Automation of the Academy of Sciences of the Czech Republic, Prague

    The workshop will be organized as a part of the conference Prague Stochastics 2019, to be held in August 19–23, 2019, in UTIA, the Institute of Information Theory and Automation of the Academy of Sciences of the Czech Republic, Prague.
    Workshop in memory of František Matúš (August 2019)

    The workshop will be devoted to František Matúš, who passed away on May 17, 2018. His research interests reached several mathematical fields. He was involved in information theory, probability theory, statistics, geometry, algebra, and matroid theory. The workshop to commemorate him is intended to be multidisciplinary, involving these fields in which František worked, and the areas close to his interests. We particularly welcome contributions devoted to information geometry, entropic regions, information inequalities, cryptography, polymatroids, optimization of convex integral functionals, discrete Markovian random sequences, conditional independence, semi-graphoids, graphical models, exponential families, and algebraic statistics.

    The workshop will take place at his home institution. Presentations at the workshop will include about ten invited talks given by experts in the area of his interest, and contributions from registered participants on close topics. A preliminary list of main speakers include:

    László Csirmaz (Renyi Institute, Budapest) Imre Csiszár (Renyi Institute, Budapest) Thomas Kahle (OvGU, Magdeburg) Seffen Lauritzen (University of Copenhagen) Carles Padró (Universitat Politecnica de Catalunya) Johannes Rauh (Max Planck Institute) Andrei Romashchenko (Laboratoire d’Informatique, Montpellier) Bernd Sturmfels (Max Planck Institute) Raymong Yeung (Chinese University of Hong Kong) Piotr Zwiernik (Barcelona)

    Shorter contributed talks or posters will be selected from submitted abstracts by the program committee. The option to present open problems within smaller topic-specific sessions, moderated by invited chairs, is also considered, and will depend on the interest expressed by the preregistered participants. No confer- ence fee is planned.

    If you are interested in participating, please use the pre-registration form

    and provide us with an abstract of a suggested presentation by May 17, 2019.

    Program Committee:

    Nihat Ay (MPI MIS, Leipzig) László Csirmaz (Renyi Institute, Budapest) Milan Studeny ́ (UTIA, Prague)

  • PhD defense- Vigneaux - 14th June2019 IMJ-PRG - Paris


    "Topology of statistical systems: a cohomological approach to information theory"
    PhD-defense of Juan Pablo Vigneaux at l'IMJ-PRG under the direction of Daniel Bennequin.

    The defense will take place on friday 14th june 2019 at 10:30 AM in room 1009 of "bâtiment Sophie Germain, 8 place Aurélie Nemours, 75013 Paris France. IMJ-PRG

    The PhD manuscript can be downloaded HERE

    PhD jury:

    Pr. Samson Abramsky, University of Oxford, Rapporteur. Pr. Daniel Bennequin, Université Paris Diderot, Directeur de thèse. Pr. Stéphane Boucheron, Université Paris Diderot, Examinateur. Pr. Antoine Chambert-Loir, Université Paris Diderot, Examinateur. Pr. Philippe Elbaz-Vincent, Université Grenoble Alpes, Rapporteur. Pr. Mikhail Gromov, Institut des Hautes Études Scientifiques, Examinateur. Pr. Kathryn Hess, École Polytechnique Fédérale de Lausanne, Examinatrice. Pr. Olivier Rioul, Télécom ParisTech, Examinateur.

  • August-November 2019 - Toulouse, France


    Toulouse August-November 2019

    Dowload POSTER

    This thematic trimester aims to highlight recent advances and scientific synergies between statistics and geometry. The dynamics of this scientific combination between statistics and geometry is driven by many applications in the field of signal processing (radar, images, …) and massive data (internet databases, monitoring, …). This thematic trimester will undoubtedly be a scientific springboard for the development of a Statistical Geometry-Computational Geometry research axis, whose efflorescence is highly probable in the next decade. The trimester will open at the end of August 2019, with the GSI 2019 conference organized at ENAC. The thematic trimester will then extend, from September to November, around the three following scientific axis:

    Information Geometry (30th of August-6th of September 2019—14th-19th of October 2019), Topology for learning and data analysis (30th of September-4th of October 2019), Computational algebraic geometry, optimization and statistical applications (6th-8th November 2019).
    Registration is free however mandatory. To register to a workshop please go to workshop pages.

    Financial support application for students are available. Please go to workshop pages.

    Poster of the thematic trimester
    Precise information of the conference and workshops

    Aug 27 – Aug 29 Geometric Science of Information (GSI 19) Aug 30 – Sep 5 Geometric Statistics Sep 30 – Oct 4 Topology for Learning and Data Analysis Oct 14 – Oct 18 Information Geometry Nov 6 – Nov 8 Computational Aspects of Geometry


    Practical Information (including how to move and lodge in Toulouse)

    Satellite events
    Preparating workshop (including geometry, topology, statistics for dummies)
    [French excellence school on Geometry and Statistics. Master class. July 2019](French excellence school on Geometry and Statistics. Master class. July 2019)

  • 23-27th september 2019 - Université de Rouen Normandie - France


    Capture du 2019-07-16 12-25-33.png


    Les Rencontres de Probabilités 2019 à Rouen constituent un événement satellite du congrès International Congress on Industrial and Applied Mathematics à Valence. Il s'agit également de l'édition 2019 étendue à une semaine des Rencontres de Probabilités qui sont organisées chaque année à Rouen depuis près de 20 ans sur les thèmes de la mécanique statistique et des systèmes de particules.

    Les thèmes principaux représentés cette année sont la géométrie aléatoire, l'analyse d'algorithmes et les systèmes de particules. Le programme comprendra des cours et des exposés sur chacun des trois domaines, ce qui permettra de réunir des spécialistes internationaux des différentes communautés et de renforcer les interactions entre elles. La participation des jeunes chercheurs est particulièrement encouragée.

    L'inscription est gratuite mais obligatoire.

    Dates :

    23-27 septembre 2019

    Lieu :

    Université de Rouen Normandie, site du Madrillet,

    UFR des Sciences et Techniques, Amphi D

    Télécharger l'affiche



    Valentin Féray, Universität Zürich Claudio Landim, CNRS/Université de Rouen Normandie & IMPA Rio Dieter Mitsche, Université Jean-Monnet-Saint-Étienne Matthias Reitzner, Universität Osnabrück Cristina Toninelli, CNRS/Université Paris Dauphine

    Exposés pléniers

    Imre Bárány, Hungarian Academy of Sciences Peter Bürgisser, Technische Universität Berlin Vincent Cohen-Addad, CNRS/Sorbonne Université Giambattista Giacomin, Université Paris Diderot Patricia Gonçalves, Universidade de Lisbon Jean-Baptiste Gouéré, Université de Tours Thierry Lévy, Sorbonne Université Ralph Neininger, Goethe-Universität Frankfurt Cyril Nicaud, Université Paris-Est Marne-la-Vallée Frank Redig, Technische Universiteit Delft Viet Chi Tran, Université de Lille Dimitrios Tsagkarogiannis, Università dell’Aquila


    Comité scientifique

    Thierry Bodineau, CNRS/École Polytechnique Anna de Masi, Universita di L'Aquila Jean-François Marckert, CNRS/Université de Bordeaux Brigitte Vallée, CNRS/Université de Caen Normandie Joseph E. Yukich, Lehigh University

    Comité d'organisation

    Pierre Calka, Université de Rouen Normandie Nathanaël Enriquez, Université Paris-Sud Xavier Goaoc, Université de Lorraine Mustapha Mourragui, Université de Rouen Normandi Ellen Saada, CNRS/Université Paris Descartes

    Équipe locale

    Edwige Auvray, CNRS/Université de Rouen Normandie Pierre Calka, Université de Rouen Normandie, Nicolas Forcadel, INSA Rouen Normandie Sandrine Halé, Université de Rouen Normandie Mustapha Mourragui, Université de Rouen Normandie Hamed Smail, Université de Rouen Normandie

    Capture du 2019-07-16 12-31-38.png

  • September 16 - 20, 2019 - Euler International Mathematical Institute, St. Petersburg, Russia


    Capture du 2019-07-16 12-43-26.png

    The Conference on Stochastic Geometry is going to be held at the Euler Mathematical Institute on September 16 - 20, 2019

    The Conference is organized and sponsored by:

    Euler Mathematical Institute Chebyshev Laboratory of St. Petersburg State University

    The goal of the conference is to bring together the researchers who have experience in stochastic geometry and/or stochastic processes, to exchange ideas, and to stimulate new collaborations.

    Organizing committee:

    I. Ibragimov Yu. Davydov D. Zaporozhets

    Confirmed Invited Speakers (as of March 1, 2019):

    Alexander Bufetov Pierre Calka Nicolas Chenavier David Coupier Serguei Dachian Sergey Foss Friedrich Goetze Francesca Greselin Julian Grote Anna Gusakova Raphael Lachieze-Rey Guenter Last Alexander Litvak Julien Randon-Furling Zhan Shi Evgeny Spodarev Joseph Yukich Vladislav Vysotsky Elisabeth Werner Sergei Zuyev

    Local coordinators:
    Nadia Zaleskaya, Tatiana Vinogradova, Natalia Kirshner:

  • Institute of Mathematics of Czech Academy of Sciences - Czech Republic



    Associate Prof. Dr.Sc. Hông Vân Lê

    DOWNLOAD PDF of the flyer of the course
    DOWNLOAD PDF of the LECTURE NOTES of the course

    Machine learning is an interdisciplinary field in the intersection of mathematical statistics and computer sciences. Machine learning studies statistical models and algorithms for deriving predictors, or meaningful patterns from empirical data. Machine learning techniques are applied in search engine, speech recognition and natural language processing, image detection, robotics etc. In our course we address the following questions:
    What is the mathematical model of learning? How to quantify the difficulty/hardness/complexity of a learning problem? How to choose a learningmodel and learning algorithm? How to measure success of machine learning?
    The syllabus of our course:

    Supervised learning, unsupervised learning Generalization ability of machine learning Support vector machine, Kernel machine Neural networks and deep learning Bayesian machine learning and Bayesian networks.

    Recommended Literature.

    S. Shalev-Shwart, and S. Ben-David, Understanding Machine Learning:
    From Theory to Algorithms, Cambridge University Press, 2014. Sergios Theodoridis, Machine Learning A Bayesian and Optimization
    Perspective, Elsevier, 2015. M. Mohri, A. Rostamizadeh, A. Talwalkar, Foundations of Machine
    Learning, MIT Press, 2012. H. V. Lˆe, Mathematical foundations of machine learning, lecture note

    During the course we shall discuss topics for term paper assignment which
    could be qualified as the exam.

    The first meeting shall take place at 10:40 AM Thursday October 2019, in the seminar room MU MFF UK (3rd floor). Anybody
    interested in the lecture course please contact me per email hvle [ at]
    for arranging more suitable lecture time.

    Location : Address: Institute of Mathematics of Czech Academy of Sciences, Zitna 25, 11567 Praha 1, Czech Republic


    Lecture course (NMAG 469, Fall term 2019-2020)

    Mathematical foundations of machine learning The first meeting: Octobber 03, Thursday, 10.40-12.10, in the seminar room MU MFF UK (3rd floor).


    Learning, machine learning and artificial intelligence
    1.1. Learning, inductive learning and machine learning
    1.2. A brief history of machine learning
    1.3. Current tasks and types of machine learning
    1.4. Basic questions in mathematical foundations of machine
    1.5. Conclusion Statistical models and frameworks for supervised learning
    2.1. Discriminative model of supervised learning
    2.2. Generative model of supervised learning
    2.3. Empirical Risk Minimization and overfittig
    2.4. Conclusion Statistical models and frameworks for unsupervised learning and
    reinforcement learning
    3.1. Statistical models and frameworks for density estimation
    3.2. Statistical models and frameworks for clustering
    3.3. Statistical models and frameworks for dimension reduction and
    manifold learning
    3.4. Statistical model and framework for reinforcement learning
    3.5. Conclusion Fisher metric and maximum likelihood estimator
    4.1. The space of all probability measures and total variation norm
    4.2. Fisher metric on a statistical model
    4.3. The Fisher metric, MSE and Cram´er-Rao inequality
    4.4. Efficient estimators and MLE
    4.5. Consistency of MLE
    4.6. Conclusion Consistency of a learning algorithm
    5.1. Consistent learning algorithm and its sample complexity
    5.2. Uniformly consistent learning and VC-dimension
    5.3. Fundamental theorem of binary classification
    5.4. Conclusions Generalization ability of a learning machine and model selection
    6.1. Covering number and sample complexity
    6.2. Rademacher complexities and sample complexity
    6.3. Model selection
    6.4. Conclusion Support vector machines
    7.1. Linear classifier and hard SVM
    7.2. Soft SVM
    7.3. Sample complexities of SVM
    7.4. Conclusion Kernel based SVMs
    8.1. Kernel trick
    8.2. PSD kernels and reproducing kernel Hilbert spaces
    8.3. Kernel based SVMs and their generalization ability
    8.4. Conclusion Neural networks
    9.1. Neural networks as computing devices
    9.2. The expressive power of neural networks
    9.3. Sample complexities of neural networks
    9.4. Conclusion Training neural networks
    10.1. Gradient and subgradient descend
    10.2. Stochastic gradient descend (SGD)
    10.3. Online gradient descend and online learnability
    10.4. Conclusion Bayesian machine learning
    11.1. Bayesian concept of learning
    11.2. Estimating decisions using posterior distributions
    11.3. Bayesian model selection
    11.4. Conclusion
    Appendix A. Some basic notions in probability theory
    A.1. Dominating measures and the Radon-Nikodym theorem
    A.2. Conditional expectation and regular conditional measure
    A.3. Joint distribution and Bayes’ theorem
    A.4. Transition measure, Markov kernel, and parameterized
    statistical model
    Appendix B. Concentration-of-measure inequalities
    B.1. Markov’s inequality
    B.2. Hoeffding’s inequality
    B.3. Bernstein’s inequality
    B.4. McDiarmid’s inequality

  • February 24 - March 13, 2020 - Fields Institute - Toronto, Canada





    3 weeks of conferences, courses and seminars at the Fields Institute for Research in Mathematical Sciences (Toronto canada) on "New Geometric Methods in Neuroscience".
    Registration for each event in the program is now available online. Please register for each event you wish to attend by purchasing the appropriate tickets.

    Workshops and Conferences

    Introduction to Geometric Modelling for Neuroscience February 24 - 28, 2020 New Mathematical Methods for Neuroscience March 2 - 6, 2020 Research Seminars: Exploring the Geometry of Neuroscience March 9 - 13, 2020

    Register online

    Organizing Committee

    Jeremie Lefebvre - Krembil Research Institute Matilde Marcolli - University of Toronto Florence Roullet - McMaster University Doris Tsao - Caltech Don Weaver - Krembil Research Institute, University Health Network


    1. Introduction to Geometric Modelling for Neuroscience February 24 - 28, 2020, The Fields Institute

    Location: Fields Institute, Room 230
    The minicourses will cover introductory material about modelling of the brain, the use of topological and geometric methods in neuroscience, and new categorical models of probability and information and of computing with potential applications to neuroscience.
    Friday February 28: Special lecture by Eugenia Cheng
    Minicourses speakers:

    Carina Curto (Pennsylvania State University) Daniela Eg​as Santander (École Polytechnique Fédérale de Lausanne) Lisbeth Fajstrup (Aalborg University) Tobias Fritz (Perimeter Institute) Sara Kalisnik (Wesleyan University) Florence Roullet (McMaster University) Giovanna Citti (Università di Bologna) Lyle Muller (University of Western Ontario)

    Other participants

    Britt Anderson (University of Waterloo) Farzad Fathizadeh (Swansea University) Lior Pachter (Caltech) Sivabal Sivaloganathan (University of Waterloo) Nora Youngs (Colby College)

    2. New Mathematical Methods for Neuroscience March 2 - 6, 2020, The Fields Institute

    Location: Fields Institute, Room 230
    The workshop will bring together neuroscientists and mathematicians from different disciplines, with the purpose of discussing the use of new mathematicalmethods based on geometry, algebraic topology, and category theory, to problems of modelling and of data analysis in neuroscience. These new mathematical approaches can complement and enrich the more traditional use of dynamical systems and differential equations, in modelling the brain at scaleslarger than single neurons, in terms of connectivity, functionality, neural coding and information flows.
    Workshop speakers:

    Britt Anderson, University of Waterloo Carina Curto, Pennsylvania State University Jorn Diedrichsen, University of Western Ontario Lisbeth Fajstrup, Aalborg University Farzad Fathizadeh, Swansea University Chad Giusti, University of Delaware Vladimir Itskov, Pennsylvania State University Matilde Marcolli, University of Toronto Lyle Muller, University of Western Ontario Tevin Rouse, Carnegie Mellon Alexander Ruys de Perez, Texas A&M University Doris Tsao, Caltech Hugh Wilson, York University Anqi Wu, Columbia University Dan Yamins, Stanford University Lai-Sang Young, Courant Institute NYU Nora Youngs, Colby College

    3. Research Seminars: Exploring the Geometry of Neuroscience March 9 - 13, 2020, The Fields Institute

    Location: Fields Institute, Room 230
    Daily seminar and discussion sessions meetings will take place, organized around specific topics, including brain modelling of healthy and clinical brain, geometric modelling of the visual cortex with contact and symplectic geometry, information measures and their use in neuroscience modelling, persistent topology of neuroscience data. These seminars are structured more in the style of informal discussions and presentations that focus more on ideas than on polished details and already established results. They are aimed at developing new interactions between geometry and neuroscience and creating opportunity for new collaborations to develop among the participants, by identifying specific questions and projects.
    The activities of this week will be organized around daily "research seminars", accompaniedby one or two main lectures.
    The seminar activities will consist of shorter informal presentations, as well as discussion sessions, round tables and other such open interaction opportunities. The names listed under each daily seminar theme will be responsible for running the activity, while all people attending the event are strongly encouraged to participate in all of these seminars.
    Seminar speakers:

    Britt Anderson, University of Waterloo Pierre Baudot, Median Technologies Florence Roullette, McMaster University Alessandro Sarti, École des hautes études en sciences sociales Sivabal Sivaloganathan, University of Waterloo Ann Sizemore Blevins, University of Pennsylvania Frances Skinner, Krembil Research Institute Dan Yamins, Stanford University Lai-Sang Young, Courant Institute NYU Nora Youngs, Colby College

  • Dunkerque, March 30th - April 3rd, 2020


    Capture du 2020-02-16 12-48-33.png

    The 9th conference of Stochastic Geometry Days 2020, associated with the French GDR project GeoSto, will take place in Dunkerque from March 30th to April 03th. This conference provides the opportunity to explore the presentations on all probabilistic and statistical aspects of Stochastic Geometry and applications in various fields including: random geometric graphs, percolation, statistical mechanics, computational geometry, random walks, random fields, point processes and spatial statistics.

    As in the previous editions, the Stochastic Geometry Days consists of two days of mini-courses and three days of talks given by invited speakers (five contributed talks will be also given by participants who wish to give a talk).

    Speakers of the mini courses (30-31th of March, 6h per speaker)

    François Baccelli (ENS Paris, INRIA) on the theme “Unimodularity” Clément Dombry (Université Franche-Comté) on the theme “Max-stable processes”

    Invited speakers of the conference (01-03 April, 45min per speaker)

    Rémi Bardenet (CNRS, Université de Lille) Quentin Berger (Sorbonne Université) Emilie Chautru (Mines ParisTech) Nicolas Curien (Université Paris-Saclay) Olivier Devillers (INRIA Nancy) Matthieu Fradelizi (Université Paris-Est) Alexandre Gaudillière (Université Aix-Marseille) Jean-Baptiste Gouéré (Université de Tours) Tom Hutchcroft (University of Cambridge) Benedikt Jahnel (Weierstrass Institute for Applied Analysis and Stochastics, Berlin) Sabine Jansen (Mathematical Institute of the University of Munich) Wolfgang König (Weierstrass Institute for Applied Analysis and Stochastics, Berlin) Irène Marcovici (Université de Lorraine) Elena Pulvirenti (Institute for Applied Mathematics, Bonn) Eliza O’Reilly (California Institute of Technology) Thierry de la Rue (Université de Rouen)

    Some practical information
    Informations about the conference room, hotels, travel and more are available on the page “Useful Informations”.
    Registrations (free of charge for all participants) are open from January 15th to March 15th, 2020 (registration before February 28th for participants who wish to give a contributed talk).

    Social activities
    Walk in the wild sand dune on Tuesday, March 31;
    Coktail on Wednesday, April 1;
    Gala dinner offered to all the participants on Thursday, April 2.

    (under the coordination of Nicolas Chenavier).

    Nicolas Chenavier (Université du Littoral Côte d’Opale) David Coupier (Université Polytechnique Hauts-de-France) Ahmad Darwiche (Université du Littoral Côte d’Opale) David Dereudre (Université de Lille) Romuald Ernst (Université du Littoral Côte d’Opale) Lucas Flammant (Université Polytechnique Hauts-de-France) Dominique Schneider (Université du Littoral Côte d’Opale) Thibaut Vasseur (Université de Lille)

    With the support of
    GDR GeoSto; Université du Littoral Côte d’Opale (ULCO); laboratoires LMPA J. Liouville, LPP, LAMAV, Labex CEMPI; ERC NEMO; ANR ASPAG et PPPP; CNRS; Fédération de Recherche Mathématique des Hauts-de-France (FMHF, FR2037 CNRS); Région Hauts-de-France.


    Capture du 2020-02-16 12-54-53.png

  • 13-17 July 2020 - Chania, crete, Greece


    W3. Complex Networks: Hidden Geometry and Dynamics
    Workshop organized by: B. Tadic and N. Gupteo

    The influence of network structure on dynamics in many complex systems has been demonstrated in numerous studies with a detailed analysis of empirical data and theoretical approaches. Recent studies of networks representing various complex systems from the brain to large-scale social dynamics have revealed their higher-order architecture, which can be described as aggregates of simplexes (triangles, tetrahedrons, and higher cliques). Beyond the standard graph theory, these hidden structures are quantified by algebraic topology methods. This Special Session will bring together presentations exploring different complex systems concerning a) the structure of simplicial complexes, including the properties of the underlying topological graph (network); b) dynamic processes in a particular or co-evolving network structure; c) information topology and graph representations of time series. We expect research results based on empirical data analysis and theoretical and numerical modeling. The aim is to cover recent developments in these areas of research and to stimulate discussion towards an in-depth understanding of the role of higher-order connectivity in the emergence of functional properties of complex systems.

  • 06/05/2020 - 08/05/2020, Porto, Portugal



    Announcement (20.3.2020)
    Due to the COVID-19 pandemic, the organizers have been forced to reschedule Entropy2020 to 22-25 September 2020 (22nd reserved for checking/on-site registration). In order to keep track of who is attending and allow us to finalize the conference program, please pre-register here as soon as possible. Please consider that we are planning an alternative long-distance participation (video presentations). For those participants not able to attend in this new date, we kindly ask you to contact us as soon as possible to do the necessary arrangements, which include video presentation, withdraw your abstract and/or cancel your registration.

    Welcome from the Chair
    The concept of entropy emerges initially from the scope of physics, but it is now clear that entropy is deeply related to information theory and the process of inference. Today, entropic techniques have found a broad spectrum of applications in all branches of science.

    The conference will be organized into six sessions, which reflect the inter-disciplinary nature of entropy and its applications:

    Statistical Physics Information Theory, Probability and Statistics Thermodynamics Quantum Information and Foundations Complex Systems Entropy in Multidisciplinary Applications

    The inter-disciplinary and multi-disciplinary nature of contributions from both theoretical and applied perspectives are welcome, including papers addressing conceptual and methodological developments, as well as new applications of entropy and information theory.

    All accepted abstracts will be published in the proceedings of the conference. Moreover, participants are cordially invited to contribute with a full manuscript to Entropy Special Issue "Entropy: The Scientific Tool of the 21st Century".

    Special Issue Submission deadline: 30 October 2020
    Papers presented at the conference will be granted a 30% discount!

    The conference is sponsored by MDPI, the publisher of the open-access journal Entropy and follows the very successful meeting Entropy 2018: From Physics to Information Sciences and Geometry held in May 2018 in Barcelona, Spain.

    We very much look forward to seeing you at this exciting meeting in Porto.

    Please feel free to download our Conference Poster.

    Conference Secretariat

    Ms. Yuejiao Hu Ms. Connie Xiong Ms. Stefanie Li
    Email: entropy2020 [at]

    Conference Chairs

    Professor J. A. Tenreiro Machado, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal, Website

    Conference Committee

    Professor Walter Lacarbonara, Department of Structural and Geotechnical Engineering , Sapienza University of Rome, Rome, Italy Professor Jose C. Principe, Computational NeuroEngineering Lab, University of Florida, Gainesville, FL, USA Prof. Dr. Gian Paolo Beretta, Department of Mechanical and Industrial Engineering, University of Brescia, Brescia, Italy Professor Geert Verdoolaege, Research Unit Nuclear Fusion, Department of Applied Physics, Ghent University, Ghent, Belgium Prof. Dr. William B. Sherwin, Evolution & Ecology Research Centre, School of Biological Earth and Environmental Science, UNSW Sydney, Sydney, Australia Prof. Dr. Ali Mohammad-Djafari, Former Research Director, CNRS, Paris, France Dr. Ginestra Bianconi, Queen Mary University of London, London, UK

    Invited Speakers

    Prof. Dr. Kevin H. Knuth, University at Albany (SUNY), Albany, NY, USA Prof. Dr. Miguel Rubi, University of Barcelona, Barcelona, Spain Prof. Dr. Philip Broadbridge, La Trobe University, Melbourne, Australia Prof. Dr. Marcel Ausloos, University of Leicester, Leicester, UK Dr. Remo Garattini, University of Bergamo, Bergamo, Italy Prof. Dr. Nihat Ay, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany Prof. Dr. Luca Gammaitoni, Università degli Studi di Perugia, Perugia, Italy Prof. Dr. Abraham Marmur, Technion-Israel Institute of Technology, Haifa, Israel Dr. Rosario Lo Franco, Università di Palermo, Italy Prof. Dr. Miguel A. F. Sanjuán, Universidad Rey Juan Carlos, Madrid, Spain

    Conference Organizers

    Nídia Caetano, Institute of Engineering (ISEP), Department of Chemical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal Carlos Felgueiras, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal Ana Maria Madureira, Instituto Superior de Engenharia do Porto (ISEP), Porto, Portugal Luís M. Afonso, ISRC (Interdisciplinary Studies Research Center) , Instituto Superior de Engenharia do Porto, Porto, Portugal Alexandra M.F. Galhano, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal


    SESSION 1. Statistical Physics
    classical, quantum, and relativistic statistical mechanics; kinetics theory; dynamical processes and relaxation phenomena; geometric applications to statistical physics; classical and quantum stochastic processes; combinatorial aspects of statistical physics and quantum field theory; quantum information and entangled states. Statistical physics of complex and disordered systems: biophysics, genomics, ecological and evolutionary systems, climate and earth models (including seismology); traffic flow; nonlinear time series analysis; big data analysis and algorithm problems; networks and graphs; random and fractal systems; pattern formation; collective phenomena in economic and social systems. Show all accepted abstracts (12) SESSION 2. Information Theory, Probability and Statistics
    Shannon entropy; Kullback-Leibler divergence; channel capacity; source coding; channel coding; algorithmic complexity theory; algorithmic information theory; information–theoretic security; information geometry; Bayesian inference; information theoretic learning; deep learning. Show all accepted abstracts (18) SESSION 3. Thermodynamics
    heat transfer; Carnot cycle; heat engine; thermodynamic temperature; entropy generation; Clausius equality/inequality; equilibrium/non-equilibrium thermodynamics; work availability; exergy; laws of thermodynamics. Show all accepted abstracts (26) SESSION 4. Quantum Information and Foundations
    quantum foundations; quantum probability and non-Kolmogorov models; entanglement entropy; Eigenstate thermalization; quantum phase transitions; topological order; black hole information paradox; the holographic principle; quantum coherent transport; quantum coherence in biological phenomena. Show all accepted abstracts (9) SESSION 5. Complex Systems
    complexity; nonlinearity; fractionality; nonlinear dynamics; fractional calculus; chaos; big data; networks; cybernetics; biology. Show all accepted abstracts (18) SESSION 6. Entropy in Multidisciplinary Applications
    signal processing and data analysis; entropy and complexity in biology; geosciences; environment; social network; economy and finance. Show all accepted abstracts (40)


  • Geo-Sci-Info

    Capture du 2020-03-09 02-09-50.png
    Appel à Candidatures
    Prix de Thèse Systèmes Complexes 2020

    Depuis 2017, l'Institut des Systèmes Complexes de Paris IdF (ISC-PIF) et ses partenaires décernent annuellement un prix de thèse dont l'objectif est de mettre à l'honneur la recherche dans le domaine des systèmes complexes et de distinguer les travaux de jeunes chercheur·euse.s particulièrement prometteur·euse.s.

    Ce prix est organisé en collaboration avec l'Académie d'Excellence "Systèmes Complexes" de l'Université Côte d'Azur et l’Institut Rhônalpin des Systèmes Complexes (IXXI), avec le soutien du CNRS et de la région Île-de-France.

    Nous vous invitons cordialement à diffuser largement cet appel ou à nominer un.e ou plusieurs candidat.e.s ayant soutenu leurs thèses entre janvier et décembre 2019, dans une école doctorale française.

    Veuillez noter que les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection.

    Pour nominer un.e ou plusieurs candidat.e.s, merci de nous transmettre leurs coordonnées en réponse de ce mail, en les mettant éventuellement en copie.

    Ci dessous, les détails de l'appel à candidature que nous vous invitons cordialement à partager.
    Télécharger le pdf de l'appel


    Les thèses éligibles auront été soutenues entre janvier et décembre 2019, dans une école doctorale française. Les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection. Les thèses rédigées en anglais sont acceptées par le jury.

    Pour pouvoir concourir à ce prix, les candidat.e.s doivent faire la preuve de la pertinence de leurs travaux par rapport à l'approche systèmes complexes et doivent aborder un ou plusieurs objets et questions propres à la recherche Systèmes Complexes : voir tous les détails du Prix de Thèse 2020 sur notre site.


    Les candidats et candidates devront remplir leur candidature en ligne avant le 22 mars 2020 à minuit.

    Documents demandés : CV et lettre de motivation, rapport du jury de thèse et pré-rapports des rapporteurs, résumé de la thèse.


    Les candidat.e.s sélectionnés seront invités à présenter leur travaux devant un jury interdisciplinaire composé de personnalités reconnues dans le domaine. Les auditions auront lieu à l'ISC-PIF (Paris 13e), le 22 mars 2020.

    Les critères pris en compte par le jury pour sélectionner les lauréat.e.s sont l’importance et l’originalité des contributions dans le domaine des systèmes complexes, la qualité du manuscrit et, éventuellement, l’interdisciplinarité du travail.


    22 mars 2020 : Date limite des dépôts de candidatures 15 mai 2020 : Annonce des finalistes 17 juin 2020 : Audition des finalistes et remise des prix
    + d'infos sur le site


    Annick Vignes, CAMS (CNRS EHESS), LISIS. Julien Randon-Furling, SAMM (Univ. Paris 1 Panthéon Sorbonne), Fédération Parisienne de Modélisation Mathématique (CNRS).


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  • White House -Kaggle - China National Center for Bioinformation - ONU


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    This website is dedicated to featuring national resources developed by ISO members to support the fight against COVID-19.

  • Mini courses and conferences IHES - Online


    Toposes online
    Capture du 2021-07-05 09-06-04.png


    Topos theory can be regarded as a unifying subject within Mathematics; in the words of Grothendieck, who invented the concept of topos, “It is the theme of toposes which is this “bed”, or this “deep river”, in which come to be married geometry and algebra, topology and arithmetic, mathematical logic and categorytheory, the world of the continuous and that of the “discontinuous” or “discrete” structures. It is what I have conceived of most broad, to perceive with finesse, by the same language rich of geometric resonances, an “essence” which is common to situations most distant from each other”.

    The event "Toposes online" represents the third edition of the main international conference on topos theory, following the previous ones "Topos à l’IHES" and "Toposes in Como".

    The format of the event is the same as that of the other two editions: it will consist of a three-day school, offering introductory courses for the benefit of students and mathematicians who are not already familiar with topos theory, followed by a three-day congress featuring both invited and contributed presentations on new theoretical advances in the subject as well as applications of toposes in different fields such as algebra, topology, number theory, algebraic geometry, logic, homotopy theory, functional analysis, and computer science.

    The main aim of this conference series is to celebrate the unifying power and interdisciplinary applications of toposes and encourage further developments in this spirit, by promoting exchanges amongst researchers in different branches of mathematics who use toposes in their work and by introducing a new generation of scholars to the subject.

    Because of the pandemic, this edition of the conference will take place entirely online. The participants may take advantage of the associated forum to discuss with each other (please register to it if you wish to post messages).

    School lecturers

    Olivia Caramello (University of Insubria and IHES) Laurent Lafforgue (IHES) Charles Rezk (University of Illinois)

    Invited speakers

    Samson Abramsky (University of Oxford) Jean-Claude Belfiore (Huawei) Daniel Bennequin (University of Paris 7) Dustin Clausen (University of Copenhagen) Jens Hemelaer (University of Antwerp) Luca Prelli (University of Padua) Peter Scholze (University of Bonn) Ivan Tomasic (Queen Mary University of London)

    Scientific and Organizing Committee

    Olivia Caramello Alain Connes Laurent Lafforgue


    We gratefully acknowledge IHES and the University of Insubria for their support; in particular, the videos of "Toposes online" will be made available through the IHES YouTube channel.



    Olivia Caramello: "Introduction to sheaves, stacks and relative toposes" VIDEO 1, VIDEO 2, VIDEO 3, VIDEO 4
    Abstract: This course provides a geometric introduction to (relative) topos theory.
    The first part of the course will describe the basic theory of sheaves on a site, the main structural properties of Grothendieck toposes and the way in which morphisms between toposes are induced by suitable kinds of functors between sites.
    The second part, based on joint work with Riccardo Zanfa, will present an approach to relative topos theory (i.e. topos theory over an arbitrary base topos) based on stacks and a suitable notion of relative site. Laurent Lafforgue: "Classifying toposes of geometric theories" VIDEO 1, VIDEO 2, VIDEO 3, VIDEO 4
    Abstract: The purpose of these lectures will be to present the theory of classifying toposes of geometric theories. This theory was developped in the 1970's by Lawvere, Makkai, Reyes, Joyal and other catagory theorists, systematising some constructions of Grothendieck and his student Monique Hakim, but it still deserves to be much better known that it actually is.
    The last part of the lectures will present new developpments due to Olivia Caramello which, based on her principle of "toposes as bridges", make the theory of classifying toposes more applicable to concrete mathematical situations : in particular, the equivalence between geometric provability and computing on Grothendieck topologies, and general criteria for a theory to be of presheaf type. Charlez Rezk: "Higher Topos Theory" VIDEO 1, VIDEO 2, VIDEO 3, VIDEO 4
    Abstract: In this series of lectures I will give an introduction to the concept of "infinity topoi", which is an analog of the notion of a "Grothendieck topos" which is not an ordinary category, but rather is an "infinity category".
    No prior knowledge of higher category theory will be assumed.

    Invited talks:

    Samson Abramsky: "The sheaf-theoretic structure of contextuality and non-locality" VIDEO
    Abstract: Quantum mechanics implies a fundamentally non-classical picture of the physical world. This non-classicality is expressed in it sharpest form in the phenomena of non-locality and contextuality, articulated in the Bell and Kochen-Specker theorems. Apart from the foundational significance of these ideas, they play a central role in the emerging field of quantum computing and information processing, where these non-classical features of quantum mechanics are used to obtain quantum advantage over classical computational models. The mathematical structure of contextuality, with non-locality as a special case, is fundamentally sheaf-theoretic. The non-existence of classical explanations for quantum phenomena corresponds precisely to the non-existence of certain global sections. This leads to both logical and topological descriptions of these phenomena, very much in the spirit of topos theory.
    This allows the standard constructions which witness these results, such as Kochen-Specker paradoxes, the GHZ construction, Hardy paradoxes, etc., to be visualised as discrete bundles. The non-classicality appears as a logical twisting of these bundles, related to classical logical paradoxes, and witnessed by the non-vanishing of cohomological sheaf invariants. In this setting, a general account can be given of Bell inequalities in terms of logical consistency conditions. A notion of simulation between different experimental situations yields a category of empirical models, which can be used to classify the expressive power of contextuality as a resource. Both quantitative and qualitative, and discrete and continuous features arise naturally. Jean-Claude Belfiore: "Beyond the statistical perspective on deep learning, the toposic point of view: Invariance and semantic information" (joint work with Daniel Bennequin) VIDEO
    Abstract: The last decade has witnessed an experimental revolution in data science and machine learning, essentially based on two ingredients: representation (or feature learning) and backpropagation. Moreover the analysis of the behavior of deep learning is essentially done through the prism of probabilities. As long as artificial neural networks only capture statistical correlations between data and the tasks/questions that have to be performed/answered, this analysis may be enough. Unfortunately, when we aim at designing neural networks that behave more like animal brains or even humans’ ones, statistics is not enough and we need to perform another type of analysis. By introducing languages and theories in this framework, we will show that the problem of learning is, first, a problem of adequacy between data and the theories that are expressed. This adequacy will be rephrased in terms of toposes. We will unveil the relation between the so-called “generalization” and a stack that models this adequacy between data and the tasks.
    Finally a five level perspective of learning with neural networks will be given that is based on the architecture (base site), a presemantic (fibration), languages, theories and the notion of semantic information. Daniel Bennequin: "Topos, stacks, semantic information and artificial neural networks" (joint work with Jean-Claude Belfiore) VIDEO
    Abstract: Every known artificial deep neural network (DNN) corresponds to an object in a canonical Grothendieck’s topos; its learning dynamic corresponds to a flow of morphisms in this topos. Invariance structures in the layers (like CNNs or LSTMs) correspond to Giraud’s stacks. This invariance is supposed to be responsible of the generalization property, that is extrapolation from learning data under constraints. The fibers represent pre-semantic categories (Culioli, Thom), over which artificial languages are defined, with internal logics, intuitionist, classical or linear (Girard). Semantic functioning of a network is its ability to express theories in such a language for answering questions in output about input data. Quantities and spaces of semantic information are defined by analogy with the homological interpretation of Shannon’s entropy (P.Baudot and D.B.). They generalize the measures found by Carnap and Bar-Hillel (1952). Amazingly, the above semantical structures are classified by geometric fibrant objects in a closed model category of Quillen, then they give rise to homotopical invariants of DNNs and of their semantic functioning. Intentional type theories (Martin-Löf) organize these objects and fibrations between them. Information contents and exchanges are analyzed by Grothendieck’s derivators. Dustin Clausen: "Toposes generated by compact projectives, and the example of condensed sets" VIDEO
    Abstract: The simplest kind of Grothendieck topology is the one with only trivial covering sieves, where the associated topos is equal to the presheaf topos. The next simplest topology has coverings given by finite disjoint unions. From an intrinsic perspective, the toposes which arise from such a topology are exactly those which, as a category, have the useful property that they are generated by compact projective objects. I will discuss some general aspects of this situation, then specialize to a specific example, that of condensed sets. This is joint work with Peter Scholze. Jens Hemelaer: "Toposes of presheaves on monoids as generalized topological spaces" VIDEO
    Abstract: Various ideas from topology have been generalized to toposes, for example surjections and inclusions, local homeomorphisms, or the fundamental group. Another interesting concept, that is less well-known, is the notion of a complete spread, that was brought from topology to topos theory by Bunge and Funk. We will discuss these concepts in the special case of toposes of presheaves on monoids. The aim is to gain geometric intuition about things that are usually thought of as algebraic.
    Special attention will go to the underlying topos of the Arithmetic Site by Connes and Consani, corresponding to the monoid of nonzero natural numbers under multiplication. The topological concepts mentioned earlier will be illustrated using this topos and some of its generalizations corresponding to maximal orders.The talk will be based on joint work with Morgan Rogers and joint work with Aurélien Sagnier. Luca Prelli: "Sheaves on T-topologies" VIDEO
    Abstract: Let T be a suitable family of open subsets of a topological space X stable under unions and intersections. Starting from T we construct a (Grothendieck) topology on X and we consider the associated category of sheaves. This gives a unifying description of various constructions in different fields of mathematics. Peter Scholze: "Liquid vector spaces" VIDEO
    Abstract: (joint with Dustin Clausen) Based on the condensed formalism, we propose new foundations for real functional analysis, replacing complete locally convex vector spaces with a variant of so-called p-liquid condensed real vector spaces, with excellent categorical properties; in particular they form an abelian category stable under extensions. It is a classical phenomenon that local convexity is not stable under extensions, so one has to allow non-convex spaces in the theory, and p-liquidity is related to p-convexity, where 0<p<=1 is an auxiliary parameter. Strangely, the proof that the theory of p-liquid vector spaces has the desired good properties proceeds by proving a generalization over a ring of arithmetic Laurent series. Ivan Tomasic:"A topos-theoretic view of difference algebra"
    Abstract: Difference algebra was founded by Ritt in the 1930s as the study of rings and modules with distinguished endomorphisms thought of as `difference operators’. Aiming to introduce cohomological methods into the subject, we view difference algebra as the study of algebraic objects in the topos of BN of difference sets, i.e., actions of the additive monoid N of natural numbers. Guided by the general principle that the G-equivariant algebraic geometry (where G is a group, monoid, groupoid or a category) should correspond to the relative algebraic geometry over the base topos BG, we develop difference algebraic geometry as relative algebraic geometry over the base topos BN. We extend the framework of Hakim’s 1970s monograph to include the theories of the fundamental group and the \’etale cohomology of relative schemes over a general base topos, and derive consequences in the difference case.

    Contributed talks:

    Peter Arndt: “Ranges of functors and geometric classes via topos theory” VIDEO Georg Biedermann (joint work with Mathieu Anel, Eric Finster, and André Joyal): “Higher Sheaves" VIDEO Ivan Di Liberti: “Towards higher topology” VIDEO

    Francesco Genovese (joint work with Julia Ramos González): “A derived Gabriel-Popescu Theorem for T-structures via derived injectives” VIDEO

    Matthias Hutzler: “Gluing classifying toposes” VIDEO

    Ming Ng (joint work with Steve Vickers): “Adelic Geometry via Topos Theory” VIDEO Rasekh Nima: “Every Elementary Higher Topos has a Natural Number Object” Axel Osmond (joint work with Olivia Caramello): “The over-topos at a model” VIDEO

    Jason Parker: “Covariant Isotropy of Grothendieck Toposes” VIDEO

    Morgan Rogers: “Toposes of Topological Monoid Actions” VIDEO

    Joshua Wrigley: “The Logic and Geometry of Localic Morphisms” VIDEO

    Riccardo Zanfa (joint work with Olivia Caramello): “Extending the topological presheaf-bundle adjunction to sites and toposes” VIDEO

    Nima Rasekh - Every Elementary Higher Topos has a Natural Number Object VIDEO

  • Institut of Advanced Study - Amsterdam - July 5th 2021


    Topological Data Analysis and Information Theory (online)
    Lecture Series
    It is our pleasure to invite you to attend the lecture series on Topological Data Analysis and Information Theory, organised by IAS fellow Fernando Nobrega Santos and Rick Quax.

    Event details of Topological Data Analysis and Information Theory (online)
    Date: Tuesday 29th June 2021 and monday 5th July 2021
    Time: 14:00 -17:30
    Organised by Fernando Nobrega Santos , Rick Quax

    High-order interactions are interactions that go beyond a sequence of pairwise interactions. Multiple approaches exist that aim to detect and quantify high-order interactions that are qualitatively different. Two of the most prominent approaches are topological data analysis (TDA) and information theory (IT). Central questions addressed in this lecture series are: what do these two approaches have in common? How can they complement each other? And what could they bring to application domains, especially in neuroscience?

    Tuesday 29 June 2021

    14:00-14:10 Opening by IAS 14.10-15.10 Lecture by Herbert Edelsbrunner: TDA for information theoretically motivated distances 15.10-15.20 Break 15:20-16:20 Lecture by Giovanni Petri: Social contagion and norm emergence on simplicial complexes and hypergraphs 16.20-16.30 Break 16:30-17:30 Lecture by Chad Giusti: A brief introduction to topological neuroscience

    Monday 5 July 2021

    14:00-14:10 Opening by IAS 14.10-15.10 Lecture by Rick Quax (UvA - IAS) Title: Brief introduction to information theory and the concept(s) of synergy 15.10-15.20 Break 15:20-16:20 Lecture by Fernando Rosas (Imperial College UK) Title: Towards a deeper understanding of high-order interdependencies in complex systems 16.20-16.30 Break 16:30-17:30 Lecture by Pierre Baudot (Median Technologies– France) Title: Information is Topology

    Each lecture will be 50 min, followed by Q&A. To participate, register below.

    Tuesday 29 June 2021

    First lecture Title: TDA for information theoretically motivated distances
    Speaker: Herbert Edelsbrunner (IST Austria) VIDEO
    Abstract: Given a finite set in a metric space, the topological analysis assesses its multi-scale connectivity quantified in terms of a $1$-parameter family of homology groups. Going beyond metrics, we show that the basic tools of topological data analysis also apply when we measure dissimilarity with Bregman divergences. A particularly interesting case is the relative entropy whose infinitesimal version is known as the Fisher information metric. It relates to the Euclidean metric on the sphere and, perhaps surprisingly, the discrete Morse properties of random data behaves the same as in Euclidean space.
    Short bio: Herbert Edelsbrunner graduated in 1982 from the Graz University of Technology. He worked in Austria from 1982 to 85, in Illinois from 1985 to 99, and in North Carolina from 1999 to 2012, before joining IST Austria in 2009. He received the Waterman Award from the NSF in 1991 and the Wittgenstein Prize from the FWF in 2018. He is a member of Academies of Sciences in the US, in Germany, and in Austria. His primary research area is computational geometry and topology. His research focus is on computational geometry and topology. Second lecture Title: Social contagion and norm emergence on simplicial complexes and hypergraphs
    Speaker: Giovanni Petri (ISI Italy)
    Abstract: Complex networks have been successfully used to describe dynamical processes of social and biological importance. Two classic examples are the spread of diseases and the emergence of shared norms in populations of networked interacting individuals. However, pairwise interactions are often not enough to fully characterize contagion or coordination processes, where influence and reinforcement are at work. Here we present recent results on the higher-order generalization of the SIS process and of the naming game. First, we numerically show that a higher-order contagion model displays novel phenomena, such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems. We then turn to the naming game as a prototypical example of norm emergence and show that higher-order interactions can create interesting novel phenomenologies, for example they can explain how --when communication among agents is inefficient-- even very small committed minorities are able to bring the system to a tipping point and flip the majority in the system. We conclude with an outlook on higher-order model, posing new questions and paving the way for modeling dynamical processes on these networks.
    Short Bio: Giovanni Petri is a Senior Research Scientist at ISI Foundation in Italy, working on topological approaches to complex networks and their underlying geometry, with special attention to the topology of brain structure and dynamics. Third Lecture Title: A brief introduction to topological neuroscience
    Speaker: Chad Giusti (University of Delaware - USA)
    Abstract: Algebraic topology has the potential to become a fundamental tool in theoretical neuroscience, building on the foundations laid by network neuroscience, natively incorporating higher-order structure and a rich mathematical tool kit for describing qualitative structure in systems. In this talk I will briefly survey how topological methods have been applied to problems in neuroscience, then briefly discuss current directions and a few major challenges I see for the field.

    Monday 5 July 2021

    First lecture: Title: Brief introduction to information theory and the concept(s) of synergy
    Speaker: Rick Quax (UvA - IAS) VIDEO
    Abstract: Quantifying synergy among stochastic variables is an important open problem in information theory. Information synergy occurs when multiple sources together predict an outcome variable better than the sum of single-source predictions. It is an essential phenomenon in biology such as in neuronal networks and cellular regulatory processes, where different information flows integrate to produce a single response, but also in social cooperation processes as well as in statistical inference tasks in machine learning. Here we propose a metric of synergistic entropy and synergistic information from first principles. Rick will start with a brief, need-to-know introduction of key information-theoretic notions (entropy, mutual information) and then move on to introducing the concept of synergistic information. He will highlight a few intuitive ways of trying to quantify synergistic information that exist today, including PID, a geometric approach, and his own proposed method.
    Short bio: Rick’s ambition is to study Complex Adaptive Systems with a focus on emergent information processing in dynamical systems. He is trying to span the spectrum from theoretical foundations to application domains, ensuring that new theory insights have direct impact on application-oriented research and vice versa. He is currently Assistant Professor in the Computational Science Lab at the University of Amsterdam and member of IAS. Second lecture: Title: Towards a deeper understanding of high-order interdependencies in complex systems
    Speaker: Fernando Rosas (Imperial College UK) VIDEO
    Abstract: We live in an increasingly interconnected world and, unfortunately, our understanding of interdependency is still rather limited. As a matter of fact, while bi-variated relationships are at the core of most of our data analysis methods, there is still no principled theory to account for the different types of interactions that can occur between three or more variables. This talk explores the vast and largely unexplored territory of multivariate complexity, and discusses information-theoretic approaches that have been recently introduced to fill this important knowledge gap.
    The first part of the talk is devoted to synergistic phenomena, which correspond to statistical regularities that affect the whole but not the parts. We explain how synergy can be effectively captured by information-theoretic measures inspired in the nature of high brain functions, and how these measures allow us to map complex interdependencies into hypergraphs. The second part of the talk focuses on a new theory of what constitutes causal emergence and how it can be measured from time series data. This theory enables a formal, quantitative account of downward causation, and introduces “causal decoupling” as a complementary modality of emergence. Importantly, this not only establishes conceptual tools to frame conjectures about emergence rigorously, but also provides practical procedures to test them on data. We illustrate the considered analysis tools on different case studies, including cellular automata, baroque music, flocking models, and neuroimaging datasets.
    Short Bio: Fernando Rosas received the B.A. degree in music composition and minor degree in philosophy (2002), the B.Sc. degree in mathematics (2006), and the M.S. and Ph.D. degree in engineering sciences from the Pontificia Universidad Católica de Chile (PUC, 2012). He worked as postdoctoral researcher at KU Leuven (Belgium), the National Taiwan University (Taiwan), and Imperial College London (UK). He received the “Academic Award” given by the Department of Mathematics of the PUC for having the best academic performance of his promotion and was the recipient of a CONICYT Doctoral Fellowship from the Chilean Ministry of Education (2008), a “F+” Scholarship from KU Leuven (2014), and a Marie Słodowska-Curie Individual Fellowship from the European Union (2017). He is currently working as Postdoctoral Researcher at the Data Science Institute and the Centre for Psychedelic Research at Imperial College London. His research interests lay in the interface between data science & AI, complexity science, cognitive science, and neuroscience. Third Lecture Title: Information is Topology
    Speaker: Pierre Baudot (Median Technologies– France) VIDEO
    Abstract: Information theory, probability and statistical dependencies, and algebraic topology provide different views of a unified theory yet currently in development, where uncertainty goes as deep as Galois's ambiguity theory, topos and motivs. I will review some foundations led notably by Bennequin and Vigneaux, that characterize uniquely entropy as the first group of cohomology, on random variable complexes and probability laws. This framework allows to retrieve most of the usual information functions, like KL divergence, cross entropy, Tsallis entropies, differential entropy in different generality settings. Multivariate interaction/Mutual information (I_k and J_k) appear as coboundaries, and their negative minima, also called synergy, corresponds to homotopical link configurations, which at the image of Borromean links, illustrate what purely collective interactions or emergence can be. Those functions refine and characterize statistical independence in the multivariate case, in the sens that (X1,...,Xn) are independent iff all the I_k=0 (with 1<k<n+1, whereas for Total correlations G_k, it is sufficient that G_n=0), generalizing correlation coefficient. Concerning data analysis, restricting to the simplicial random variable structure sub-case, the application of the formalism to genetic transcription or to some classical benchmark dataset using open access infotopo library, unravels that higher statistical interactions are nonetheless omnipresent but also constitutive of biologically relevant assemblies. On the side of Machine learning, information cohomology provides a topological and combinatorial formalization of deep networks' supervised and unsupervised learning, where the depth of the layers is the simplicial dimension, derivation-propagation is forward (co-homological).
    Short bio: Pierre Baudot was graduated in 1998 from Ecole Normale Supérieure Ulm magister of biology, and passed his PhD in electrophysiology of visual perception studying learning information coding in natural condition. He started to develop information topology with Daniel Bennequin at Complex System Institute and Mathematical Institute of Jussieu from 2006 to 2013, and then at the Max Planck Institute for Mathematic in the Science at Leipzig. He then joined Inserm at Marseille to develop data applications notably to transcriptomics. Since 2018, he works at Median Technologies, a medical imaging AI company, to detect and predict cancers from CT scans. He received the K2 trophy (mathematics and applications 2017), and best entropy paper prize 2019 for his contributions to topological information data analysis.

  • European Institute for Theoretical Neuroscience (EITN) - Paris - May 5-6th 2021


    energy_entropy (1).jpg


    "Energy and entropy concepts to characterize and understand brain activity"
    Organizers: Alain Destexhe, Jennifer Goldman, Mavi Sanchez-Vives, Pier Stanislao Paolucci, Chiara DeLuca, Cristiano Capone, Trang-Anh Nghiem

    Abstract: Brain circuits in vivo or in vitro display a wide variety of activity states, and they may also respond to external inputs differently in each state. The goal of this workshop is to evaluate and compare tools to understand both the activity state, and its responsiveness, based on energy and entropy concepts commonly used in physics. We will review different ways of defining relevant measures of energy and entropy, and how they apply to analyze brain activity, including brain states and cognitive tasks, and what useful information can be extracted to better understand the system. We will also evaluate the opportunity of writing a collaborative paper that reviews the different techniques and their usefulness.

    List of confirmed speakers:

    Pierre Baudot (U Marseille) Alessandra Camassa (U Barcelona) Cristiano Capone (INFN, Rome) Chiara Cirelli (U Wisconsin) Chiara de Luca (INFN, Rome) Athena Demertzi (Universite de Liege) Alain Destexhe (CNRS) Adrienne Fairhall (U Washington) Karl Friston (UCL) Jennifer Goldman (CNRS) Viktor Jirsa (AMU) Daniele Marinazzo (Ghent U) Olivier Marre (INSERM) Maurizio Mattia (ISN Roma) Thierry Mora (ENS) Trang-Anh Nghiem (ENS) Pier-Stanislao Paolucci (U Roma) Antonio Pazienti (ISN Roma) Mavi Sanchez-Vives (U Barcelona) Simone Sarasso (U Milan) Elad Schneidman (Weizmann Inst)

    Updated program:

    Day 1 - May 5

    Session 1: Theoretical aspects of energy and entropy I

    13h: general intro (Alain) and tribute to Paolo Del Giudice 13h15-13h40: Alain Destexhe (CNRS, Paris-Saclay U): "Energy and Entropy-based methods to characterize brain states" VIDEO 13h40-14h05: Chiara de Luca (INFN, Rome): "Deep-Sleep Memory Density-based-clustering: Theoretical Framework and Energetic/Entropic Effects on Awake Activity" 14h05-14h30: Thierry Mora (ENS, Paris): "Sensory adaptation and entropy production" 14h30-14h40: general discussion 14h40-15h coffee break

    Session 2: Applications to neural data I

    15h-15h25: Simone Sarasso (U Milan): "Assessing brain states through complexity: converging empirical evidence" 15h25-15h50: Athena Demetrzi (U Liege): "Cerebral configuration in states of reduced reportability" 15h50-16h15: Mavi Sanchez-Vives (IDIBAPS, Barcelona): "Cellular and synaptic contributions to cortical complexity in different brain states" 16h15-16h40: Chiara Cirelli (U Wisconsin): "Sleep and synaptic down-selection" VIDEO 16h40-16h50: general discussion 16h50-17h10 coffee break

    Session 3: Theoretical aspects of energy and entropy II

    17h10-17h35: Daniele Marinazzo (Ghent U): "Beyond pairwise interactions: expanding the transfer entropy and the mutual information" VIDEO 17h35-18h: Trang-Anh Nghiem (ENS, Paris): "Unveiling the correlation structure supporting brain states and neural codes with maximum entropy models" VIDEO 18h-18h25: Jennifer Goldman (CNRS, Paris-Saclay U): "A scale-integrated approach to brain states; from single neuron biophysics to macroscopic neural dynamics" VIDEO 18h30-18h40: general discussion

    DAY 2 - May 6

    Session 4: Theoretical aspects of energy and entropy III

    13h-13h25: Elad Schneidman (Weizmann Inst): "Learning the code of large neural populations by sparse nonlinear random projections" 13h25-13h50: Pierre Baudot (AMU Marseille): "Topological information and higher order statistical structures: mathematical foundations of information and deep learning" VIDEO 13h50-14h15: Maurizio Mattia & Antonio Pazienti (ISN Roma): "Insights in the transition from slow-wave activity to wakefulness using an entropy-based index" VIDEO 14h15-14h25: general discussion
    14h25-14h45 coffee break

    Session 5: Applications to neural data II

    14h45-15h10: Olivier Marre (INSERM, Paris): "Maximum entropy models in the retina" 15h10-15h35: Cristiano Capone (INFN, Rome): "Simulations approaching data: Cortical slow waves in inferred models of the whole hemisphere of mouse" VIDEO 15h35-16h: Alessandra Camassa (IDIBAPS, Barcelona): "Energy-based hierarchical clustering of cortical slow waves in multi electrode recordings" VIDEO 16h-16h10: general discussion 16h10-16h30 coffee break

    Session 6: Theoretical aspects of energy and entropy IV

    16h30-16h55: Karl Friston (UCL, London): "Deep inference" VIDEO 16h55-17h20: Adrienne Fairhall (U Washington) 17h20-17h45: Viktor Jirsa (AMU, Marseille): "Entropy, symmetry & dynamics" VIDEO
    17h45-18h30: general discussion and next steps


Geometric Science of Information