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Group Details

Geometric Science of Information

The objective of this group is to bring together pure/applied mathematicians, physicist and engineers, with common interest for Geometric tools and their applications. It notably aim to organize conferences and to promote collaborative european and international research projects, and diffuse research results on the related domains. It aims to organise conferences, seminar, to promote collaborative local, european and international research project, and to diffuse research results in the the different related interested domains.

  • Geo-Sci-Info

    The division of science at New York University in Abu Dhabi is currently advertising two-years postdoctoral positions to work with me on subjects in high dimensional probability and related fields.
    More information can be found at the following link: where the applications should be submitted. Please do not hesitate to share the message with potential candidates and feel free to contact me directly for further information.

    The anticipated start date is September 1, 2020. Applications should be received before March 15 to be guaranteed full consideration. (Candidates should please ask their referees to send their letters of reference by that date.)

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  • Geo-Sci-Info

    Open GPU Data Science | RAPIDS


    The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.
    Learn more

    Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask.
    Learn more about Dask

    Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.

    Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

    Drastically improve your productivity with more interactive data science.
    Learn more about XGBoost

    RAPIDS is an open source project. Supported by NVIDIA, it also relies on numba, apache arrow, and many more open source projects.
    Learn more

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  • Geo-Sci-Info

    Capture du 2020-02-16 13-55-41.png
    This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

    It provides the following solvers:

    • OT Network Flow solver for the linear program/ Earth Movers Distance [1].
    • Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional * GPU implementation (requires cupy).
    • Sinkhorn divergence [23] and entropic regularization OT from empirical data.
    • Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
    • Non regularized Wasserstein barycenters [16] with LP solver (only small scale).
    • Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
    • Optimal transport for domain adaptation with group lasso regularization [5]
    • Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
    • Linear OT [14] and Joint OT matrix and mapping estimation [8].
    • Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
    • Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
    • Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
    • Non regularized free support Wasserstein barycenters [20].
    • Unbalanced OT with KL relaxation distance and barycenter [10, 25].

    Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.

    Using and citing the toolbox
    If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference:

    title={POT Python Optimal Transport library},
    author={Flamary, R{'e}mi and Courty, Nicolas},

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  • Geo-Sci-Info

    lease find below the announcement of three positions (two M2
    internships and one post-doc position) opened in Nancy :


    PhD opening at the University of Groningen

    The group of Probability and Statistics at the University of Groningen invites applications for a

    4-year PhD position

    During your PhD you will work at the forefront of research in topological data analysis and spatial random networks. While the previous years have brought forth outstanding progress in the description of large combinatorial random graphs, the understanding of random structures with spatial dependencies is still in its infancy. A key component of your research will be to identify regimes that are amenable to a rigorous mathematical description in terms of central limit theorems and large deviation principles.

    Modern materials science is a major driving force behind the interest in topological data analysis and spatial random networks, since the insights gained from appropriate mathematical models make it possible to reduce the number of time-consuming experiments. With the newly established CogniGron, the University of Groningen has created an interdisciplinary research center with the goal of making progress towards cognitive computing. Hence, you will have the unique opportunity to interact broadly with colleagues outside mathematics. You will also be expected to contribute to the teaching at the faculty.

    Hence, if you have graduated with an outstanding Master's degree (or are about to do so soon) and are committed to working on fundamental research questions, we are very much looking forward to your application. Any background knowledge in large deviation theory, random networks or central limit theorems will help you to get a smoother start.

    We offer a full-time PhD position for 4 years. The salary and working hours are determined according to the Collective Labour Agreement for Dutch Universities. In 2019, PhD candidates started with € 2,325 gross per month; for a candidate in the fourth year, this figure rises to € 2,972. In addition to that, the salary scheme comprised 8% of the yearly salary as holiday bonus and 8.3% as end of your bonus.

    We are an equal opportunity employer that values diversity. We have adopted an active policy to increase the number of female scientists across all disciplines of the university.

    We envision that you join us on April 1, 2020 or later. Please send us until January 27, 2020 your cover letter, CV, Master's thesis (or a preliminary version) and transcript of records. All documents should be sent to dr. Christian Hirsch:, who will also be happy to answer your questions on the position.



    Applications are invited for a StatLab–CANSSI–CRM Postdoctoral Fellowship in Statistics. To be eligible, candidates should have received their PhD after June 30, 2016, and must have fulfilled all PhD requirements by the time of taking up the award. The successful candidate must be supervised by at least one Regular Member of StatLab and be based in a member’s institution for the tenure of the award. A list of StatLab members can be found here:

    The fellowship is worth $45 000. A successful candidate will be designated a StatLab–CANSSI–CRM Postdoctoral Fellow for a period of one year, starting any time between June 1 and September 30, 2020.

    An application includes: (i) a presentation letter including in particular a description of research interests and research program; (ii) the candidate’s CV; (iii) electronic copies of articles, theses, research reports, etc. Candidates must prepare the application using the guidelines presented here:

    and send it to by January 31, 2020. Candidates should arrange as well to have at least two letters of recommendation sent independently.

    PDF announcement is enclosed to this email.


    Un appel à candidatures est lancé pour une bourse postdoctorale StatLab–INCASS–CRM en statistique. Pour être éligible, un candidat doit avoir obtenu son doctorat après le 30 juin 2016 et avoir complété toutes les exigences du programme de doctorat au moment de débuter le stage. Il ou elle doit être supervisé par au moins un membre régulier de StatLab et être inscrit dans l’établissement d’un tel membre pendant la période de validité de la bourse. Pour une liste complète des membres de StatLab, voir

    La bourse est d’une valeur de 45 000$. Le boursier portera le titre de stagiaire postdoctoral StatLab–INCASS–CRM pendant une période d’un an pouvant commencer à tout moment entre le 1er juin et le 30 septembre 2020.

    Un dossier de candidature comprend: (i) une lettre de présentation incluant notamment une description des intérêts de recherche, un programme de recherche; (ii) le CV du candidat; (iii) copies électroniques d'articles, thèse, rapport de recherche, etc. Les candidats doivent préparer leur dossier en suivant les indications disponibles ici:

    et envoyer leur dossier à avant le 31 janvier 2020. Les candidats doivent également s'arranger pour transmettre au moins deux lettres de recommandation, envoyées indépendamment.

    L'annonce officielle au format pdf est jointe à ce courriel.

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  • Geo-Sci-Info

    Lors de la prochaine campagne synchronisée, un poste MCF 26/27
    "Statistique pour la science des données" sera publié à l'IUT
    d'Avignon, département STID. La personne recrutée intègrera le
    département STID et l'équipe de Statistique du Laboratoire de
    Mathématiques d'Avignon (LMA). Le profil détaillé est disponible via le
    lien :

    Je vous remercie par avance de faire suivre cette annonce autour de
    vous. Nous invitons toutes les personnes intéressées à contacter
    Delphine Blanke (responsable STID & équipe de Statistique) et Céline
    Lacaux (directrice du LMA).

    Amitiés, Céline.

    PS : Je vous prie de nous excuser pour les réceptions multiples de cette

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  • Geo-Sci-Info


    The Faculty of Electrical Engineering, Mathematics and Computer Science
    at the University of Twente, The Netherlands, invites applications for a
    fully funded

    PhD studentship in spatial statistics

    in the project `Data driven risk management for fire services' funded
    by the Dutch Research Council.

    This project aims to develop a flexible hierarchical statistical
    framework for the analysis and prediction of various types of fires
    and will be carried out in close cooperation with the Twente Fire
    Brigade. It will deliver a model for spatio-temporal inference
    (including monitoring, filtering and prediction) that is able to
    recognize patterns in historic data and convert these to a prediction.
    The results can be used for both repression and prevention purposes,
    including improving vehicle and personnel planning in the fire stations,
    informing the right people at the right time and evaluating the
    effectivity of public awareness campaigns.

    Candidates are required to have a Masterʼs degree (or an equivalent one)
    in mathematics or a related discipline and a solid background in
    probability and statistics. They should have excellent grades, research
    talent (as proven by their thesis), an excellent command of English and
    good academic writing and presentation skills.

    The terms of employment and salary are in accordance with the Dutch
    Collective Labour Agreement for universities.

    To apply, please send a letter of motivation, a list of your BSc and
    MSc courses with grades and either a copy of your Master's thesis if
    available or a copy of your Bachelor's thesis accompanied by an outline
    of your Master's thesis to M. de Graaf (m.degraaf [at] or
    Prof.dr. M.N.M. van Lieshout (colette [at]

    from whom further information can also be obtained.

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  • Geo-Sci-Info

    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.

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    S3. Information Geometry and Deformed Statistics
    Special Session organized by J. Zhang and H. Matsuzoe

    Information geometry provides a suite of differential geometric tools for studying statistical inference, information theory, and machine learning models. Key notions such as statistical manifolds (with Fisher information as to its Riemannian metric), Hessian geometry, biorthogonal coordinates have links to statistical mechanics, thermodynamics, geometric mechanics, etc. As sequel to the special session (organized by Johnston, Matsuzoe, Ruppeiner, and Wada) at SigmaPhi2017, this Session at SigmaPhi2020 will explore differential geometric characterizations of probabilistic models arising from a variety of setting including stochastic thermodynamics, condensed matter physics, cosmology and high-energy physics, etc. Of particular interest are deformed statistical models, models which deviate from exponential family through parameterization (e.g., kappa-exponential model, q-exponential model, Renyi model) and their associated deformed entropy, cross-entropy, and divergences. The Session will also welcome contributions from related disciplines of statistical machine learning, dynamics and control, optimal transport and Wasserstein geometry, etc.

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  • Geo-Sci-Info


    13-17 July 2020 - Chania, Crete, Greece

    The Conference is organized in the following three Areas to cover all the Topics of Statistical Physics:

    Area A: Foundations and Theoretical aspects of classical, quantum and relativistic statistical physics and thermodynamics. Mathematical aspects and methods, formalism, rigorous results, exact solutions, connections with the methods of high energy physics, string theory, mathematical statistics and information theory, information geometry, classical, quantum and relativistic transport theory, Boltzmann and Fokker-Planck kinetics, nonlinear kinetics, dynamical systems, relaxation phenomena, random systems, pattern formation, fractal systems, solitons, chaotic systems, strongly correlated electrons, soft quantum matter, mesoscopic quantum phenomena, fractional quantum Hall effect, low dimensional quantum field theory, quantum phase transitions, quantum information and entanglement, power laws, stochastic optimal controletc.

    Area B: Applications to Physical Systems: quantum systems, soft condensed matter, liquid crystals, plasmas, fluids, surfaces and interfaces, disordered and glassy systems, percolation, spin glasses, structural glasses, jamming, critical phenomena and phase transitions,fluids and interfacial phenomena, molecular and ionic fluids, metastable liquids, hydrodynamic instabilities, turbulence, growth processes, wetting, surface effects, films, crystals, confined systems, surfaces and interfaces, chemical reactions, cold atoms, etc.

    Area C: Applications to non-Physical Systems: Interdisciplinary applications of statistical physics, networks and graphs, applied networks, biophysics, genomics, environments, climate and earth models, seismology, linguistics, econophysics, social systems, traffic flow, algorithmic problems, complex systems, nonlinear time-series analysis, novel data analysis tools, extreme events, tipping points, prediction, classification, etc.

    Any area include several Sessions dealing with general aspects and applications.

    Special sessions: Some special sessions within the thematic Area A will cluster talks dedicated to the following theoretical topics:

    • S1: Spin glass theory and far beyond;
    • S2: Quantum long-range interactions;
    • S3: Information geometry and deformed statistics;
    • S4: Entropies and correlations in complex systems.

    Workshops: Some workshops will cluster talks dedicated to special applications of statistical physics in physical and non-physical systems and will be organized as parallel events.

    Poster Sessions: The Conference will also include some poster sessions.

    Conference Chairman

    • Kaniadakis G. (Politecnico di Torino, Italy)

    Organizing Committee

    • Argyrakis P. (Aristotle University of Thesaloniki, Greece)
    • Carbone A. (Politecnico di Torino, Italy)
    • Constantoudis V. (NCSR Demokritos, Athens, Greece)
    • Ellinas D. (Technical University of Crete, Chania, Greece)
    • Gervino G. (University of Torino, Italy)
    • Hristopulos D. (Technical University of Crete, Chania, Greece)
    • Lapenta G. (Katholieke University of Leuven, Belgium)
    • Lissia M. (INFN, University of Cagliari, Italy)
    • Maizza G. (Politecnico di Torino, Italy)
    • Scarfone A.M. (ISC-CNR, Politecnico di Torino, Italy) - Chair
    • Spagnolo B. (University of Palermo, Italy)
    • Valenti D. (Università degli studi di Palermo, Italy)

    EPS-SNLP, European Physical Society Statistical and Nonlinear Physics Division

    • Beck C. (Queen Mary, University of London, UK) - Chair
    • Caldarelli G. (IMT School for Advanced Studies Lucca, Italy)
    • Cugliandolo L.F. (Université Pierre et Marie Curie, Paris, France)
    • Gudowska-Nowak E. (Jagiellonian University, Krakow, Poland)
    • Kantz H. (Max Planck Institute, Dresden, Germany)
    • Manneville P. (Ecole Polytechnique, Palaiseau, France)
    • Ruffo S. (SISSA, Trieste, Italy)
    • Toral R. (Inst. de Física Interdisc. y Sist. Compl., Palma de Mallorca, Spain)

    Abstract Submisision

    Registration and Payment
    Early registration (from 17 April to 13 May)
    Registration fee: 500€ (VAT included)
    EPS member registration fee: 450€ (VAT included)
    PhD Students: 250€ (VAT included)
    Regular registration (after 13 May)
    Registration fee: 600€ (VAT included)
    EPS member registration fee: 500€ (VAT included)
    PhD Students: 400€ (VAT included)
    The registration fee for the Participants includes: Admission to all scientific sessions, Conference Kit, Abstract Booklet, Coffee Breaks.

    Selected contributions (invited, oral and poster) can be invited to be submitted for publication in topical volumes of ISI quoted journals, devoted to the SigmaPhi2020 Conference.
    All the papers will undergo the standard referral process of the journals. The submission of a paper implies that it represents original work not previously published and not considered for publication elsewhere. Further details concerning journals, manuscript length and format will be given during the Conference.


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    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

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