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

    OFFICIAL WEBPAGE

    Period
    March 27–29, 2019

    Place
    Rm.~509, Mathematics Bldg., Nagoya University

    Speakers

    • 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) math.nagoya-u.ac.jp)

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    APPLICATION WEBSITE

    What you will do

    As part of the Research team, you will embrace theoretical mathematics, computer science and financial knowledge. Fully involved on advanced topics, you will work closely with researchers, deep learners, and science addicts. Being part of our international team, you will experience how being smartly wrong often brings to a better solution.

    Main missions:

    improve the learning capabilities of the automated trading systems
    design solutions to challenge large datasets with a data driven approach
    contribute to the research infrastructure aiming at identifying financial biais
    challenge researchers and common knowledge
    suggest and engage in team collaborations to meet research goals
    report and present research findings and developments

    Skills we are looking for

    PhD or MS in Data Science, Science Technology or Mathematics.

    You have:

    3-5 years of experience in Machine Learning
    a powerful intellectual curiosity with a strong academic knowledge in probability, statistics and machine learning models
    experience with Python3 and libraries such as Numpy, Pandas, Plotly
    experience with Linux and Git environments
    strong knowledge of object-oriented programming and algorithms
    a “can-do” attitude and a problem-solving mindset
    eager to learn and to challenge complex machine learning problems
    an ability to operate in an agile and fast-paced environment

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    APPLICATION WEBSITE
    Job Overview

    Advanced Analytics - Sr. Data Scientist will execute advanced computational approaches to aid in evidence-based pharmaceutical product development. He/She will leverage high-dimensional population health data to support R&D, Medical, HEVA, commercial product development, access and business strategy. The Advanced Analytics role will generate analytics required by healthcare decision makers to support patient access and use of Sanofi medicines and he/she will contribute to the insights required by Sanofi internal teams to develop and commercialize the most impactful medicines.

    Job Responsibilities

    Get to apply a broad array of capabilities spanning machine learning, statistics, text-mining/NLP, and modeling to extract insights to structured and unstructured healthcare data sources, pre-clinical, clinical trial and complementary real world information streams.
    Work on a variety of team-based projects providing expertise in analytical and computational approaches.
    Have the opportunity to identify novel solutions to internal analytics & data challenges including the piloting and/or evaluation of tools for analytics, reporting and data visualization.
    Develop additional skills through training courses, mentoring, and interactions daily with team members and Sanofi stakeholders.
    Provide expertise and execute advanced analytics for solving problems across R&D, Medical Affairs, HEVA and Market Access Strategies and Plans.
    Design and implement data models, perform statistical analysis and create predictive analysis models
    Translate and appropriately champion advanced analytics results and capabilities to non-technical audiences.
    Work with internal and external data scientists to scope and execute Advance Analytics projects.

    Essential Skills & Experience

    PhD or ScD in quantitative field such as Health Services research, Medical Economics, Medical Informatics, Biostatistics, or Computer Science, computer engineering or related field with a minimum of 3 years of industry or academic experience
    Relevant Masters Degree, with 6 or more years of related industry experience
    Proficiency in at least two or more technical or analytical languages (R, Python, etc..) and a willingness to embrace new coding approaches.
    Experience with advanced ML techniques (neural networks/deep learning, reinforcement learning, SVM, PCA, etc.).
    Demonstrated ability to interact with a variety of large-scale data structures e.g. HDFS, SQL, noSQL
    Experience working across multiple environments (e.g. AWS, GCP, linux) for optimizing compute and big data handling requirements.
    Experience with any of the following biomedical data types/population health data/real world data/novel data streams.
    Strong oral and written communication skills
    A demonstrated ability to work and collaborate in a team environment

    Desirable Skills & Experience

    Ability to prototype analyses and algorithms in high-level languages embracing reproducible and collaborative technology platforms (e.g. github, containers, jupyter notebooks)
    Exposure to NLP technologies and analyses
    Knowledge of some datavis technologies (ggplot2, shiny, plotly, d3, Tableau or Spotfire)

    Sanofi is committed to welcoming and integrating people with disabilities

    At Sanofi diversity and inclusion is foundational to how we operate and embedded in our Core Values. We recognize to truly tap into the richness diversity brings we must lead with inclusion and have a workplace where those differences can thrive and be leveraged to empower the lives of our colleagues, patients and customers. We respect and celebrate the diversity of our people, their backgrounds and experiences and provide equal opportunity for all.

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    APPLICATION WEBSITE

    THE ROLE

    You will join the Data Science team. It's a cross-functional team using data to support strategic decision-making and build better experiences for our passengers and drivers alike.

    As a Data Scientist focused on Algorithms, you will apply machine learning and optimisation techniques on our rich datasets to solve some of the most interesting mobility challenges, such as dynamic pricing, intelligent allocation and much more!

    WHAT YOU'LL DO

    • Work with Product to identify and prioritise algorithmic needs

    • Team up with Engineering to incorporate machine learning and optimisation algorithms in our product

    • Code simulation modules to replicate driver and passenger behaviours and suggest pricing or dispatch improvements

    • Uncover hidden opportunities for growth and efficiency for Heetch

    • Conduct and present quantitative analysis that results in actionable recommendations

    WHO WE ARE LOOKING FOR

    • You have a degree in Computer Science, Engineering, Economics, Physics, Statistics or another quantitative field (MS and above preferred)

    • You have 2+ years of industry experience in algorithm design and development

    • You are comfortable manipulating large datasets (using SQL, Python, R etc)

    • You can build and fit statistical, machine learning, or optimisation models

    • You can collaborate with Engineers to turn prototypes into scaled-up products

    • You can communicate effectively with colleagues from various backgrounds and technical levels

    • You are fluent in English

    BONUS POINTS IF YOU:

    • Have prior exposure to startup environments

    • Have experience with cloud computing and big data frameworks (incl. geospatial data)

    • Have experience leading machine learning projects and/or building data products end-to-end under limited supervision

    • Are able to model and run simulated and live traffic experiments

    • Are an explorer and enjoy going out!

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    UMAP - Leland McInnes, John Healy, James Melville

    GITHUB - OFFICIAL WEBSITE

    Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data

    • The data is uniformly distributed on a Riemannian manifold;
    • The Riemannian metric is locally constant (or can be approximated as such);
    • The manifold is locally connected.

    From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.

    The details for the underlying mathematics can be found in our paper on ArXiv:

    • McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018

    The important thing is that you don't need to worry about that -- you can use UMAP right now for dimension reduction and visualisation as easily as a drop in replacement for scikit-learn's t-SNE.

    Documentation is available via ReadTheDocs.
    Installation, licence, how to use information is avalaible on
    GITHUB - OFFICIAL WEBSITE](https://github.com/lmcinnes/umap)

    Benefits of UMAP

    UMAP has a few signficant wins in its current incarnation.

    • First of all UMAP is fast. It can handle large datasets and high dimensional data without too much difficulty, scaling beyond what most t-SNE packages can manage.
    • Second, UMAP scales well in embedding dimension -- it isn't just for visualisation! You can use UMAP as a general purpose dimension reduction technique as a preliminary step to other machine learning tasks. With a little care (documentation on how to be careful is coming) it partners well with the hdbscan clustering library.
    • Third, UMAP often performs better at preserving aspects of global structure of the data than t-SNE. This means that it can often provide a better "big picture" view of your data as well as preserving local neighbor relations.
    • Fourth, UMAP supports a wide variety of distance functions, including non-metric distance functions such as cosine distance and correlation distance. You can finally embed word vectors properly using cosine distance!
    • Fifth, UMAP supports adding new points to an existing embedding via the standard sklearn transform method. This means that UMAP can be used as a preprocessing transformer in sklearn pipelines.
    • Sixth, UMAP supports supervised and semi-supervised dimension reduction. This means that if you have label information that you wish to use as extra information for dimension reduction (even if it is just partial labelling) you can do that -- as simply as providing it as the y parameter in the fit method.
    • Finally UMAP has solid theoretical foundations in manifold learning (see our paper on ArXiv). This both justifies the approach and allows for further extensions that will soon be added to the library (embedding dataframes etc.).

    Performance and Examples

    UMAP is very efficient at embedding large high dimensional datasets. In particular it scales well with both input dimension and embedding dimension. Thus, for a problem such as the 784-dimensional MNIST digits dataset with 70000 data samples, UMAP can complete the embedding in around 2.5 minutes (as compared with around 45 minutes for most t-SNE implementations). Despite this runtime efficiency UMAP still produces high quality embeddings.

    The obligatory MNIST digits dataset, embedded in 2 minutes and 22 seconds using a 3.1 GHz Intel Core i7 processor (n_neighbors=10, min_dist=0 .001):

    UMAP embedding of MNIST digits

    umap_example_mnist1.png

    The MNIST digits dataset is fairly straightforward however. A better test is the more recent "Fashion MNIST" dataset of images of fashion items (again 70000 data sample in 784 dimensions). UMAP produced this embedding in 2 minutes exactly (n_neighbors=5, min_dist=0.1):

    UMAP embedding of "Fashion MNIST"
    umap_example_fashion_mnist1.png

    The UCI shuttle dataset (43500 sample in 8 dimensions) embeds well under correlation distance in 2 minutes and 39 seconds (note the longer time required for correlation distance computations):

    UMAP embedding the UCI Shuttle dataset
    umap_example_shuttle.png

    posted in GSI FORGE read more
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    Shape Analysis, Stochastic Mechanics and Optimal Transport
    OFFICIAL WEBSITE

    • 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 https://arxiv.org/abs/1805.11572
      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 https://research.math.osu.edu/networks/formigrams/
      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

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    Shape Analysis, Stochastic Mechanics and Optimal Transport
    OFFICIAL WEBSITE

    Organizers

    Description

    The Banff International Research Station will host the "Shape Analysis, Stochastic Geometric Mechanics and Applied Optimal Transport" workshop from December 9th to December 14th, 2018.

    The comparison and analysis of shapes, whether of organs, cells or engineering structures such as airfoils, pose important mathematical and statistical challenges. Shape analysis has recently seen a tremendous development in both theory and practice, driven by a wide range of applications from biological imaging to fluid dynamics. For example, organ shapes observed in medical images can now be used for diagnostic and prognostic purposes, and optimization of shapes has become an important tool in engineering.

    On the theoretical side, recent developments have highlighted the strong connections between shape analysis and the related fields of optimal transport and stochastic geometric mechanics, both very active fields in their own right. The workshop aims to bring together researchers in these three fields, to share methodological developments and open problems, and generally to link and accelerate research in all three fields.

    The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).

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    Journée ISS France
    OFFICIAL WEBSITE
    iss.png

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

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

    Organisateurs

    • 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

    Inscriptions
    La participation est gratuite mais l'inscription est obligatoire. Vous pouvez vous inscrire ou proposer une communication par par mail à l'adresse: bruno.figliuzzi-at-mines-paristech.fr

    Description

    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.

    posted in ISS 2019 read more
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    Applications are invited for a 12-month-postdoctoral fellowship in discrete probability at Université Jean Monnet, Institut Camille Jordan. The position is funded by IDEX Lyon IMPULSION (you may also click on this link for more details: https://math.unice.fr/~dmitsche/Postdoc.pdf)

    Research areas: random graphs, random geometric (hyperbolic) graphs, random walks, discrete probability in general.

    Practical informations:

    • The position is for one year (12 months) with a salary of approx. 27K Euro (in the case of less than 3 years experience after the PhD) and approx. 34K Euro (between 3 and 6 years experience after the PhD).
    • Financial support to attend workshops / invite collaborators will be granted.
    • No teaching
    • A lot of activity around discrete probability going on
    • The starting date is flexible, between February and September 2019. The candidate should indicate the preferred starting date in the application letter.

    Application and deadline:
    Applications including a CV, a list of publications and an approximately two-page description of research interests should be sent by email to Dieter Mitsche: dmitsche [at] gmail.com.
    Applicants should also arrange two recommendation letters (to be sent to the same address). Informal inquiries can be sent to the same address. Deadline for applications: January 15th, 2019, and then on a rolling basis

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    APPLICATION WEBPAGE

    Job description

    The University of Limerick (UL) with over 15,000 students and 1,400 staff is an energetic and enterprising institution with a proud record of innovation and excellence in education, research and scholarship. The dynamic, entrepreneurial and pioneering values which drive UL’s mission and strategy ensures that we capitalise on local, national and international engagement and connectivity. We are renowned for providing an outstanding student experience and conducting leading edge research. Our commitment is to make a difference by shaping the future through educating and empowering our students. UL is situated on a superb riverside campus of over 130 hectares with the River Shannon as a unifying focal point. Outstanding recreational, cultural and sporting facilities further enhance this exceptional learning and research environment.

    Applications are invited for the following position:

    Faculty of Science + Engineering

    Department of Mathematics & Statistics

    Lecturer and Lecturer below the bar in Statistics & Data Analytics

    Contract Type: Lecturer – Multiannual (Post 1)

    Contract Type: Lecturer below the bar (Post 2) - Tenure Track (five year fixed term).

    During the term of the contract the successful applicant will have the opportunity to apply for tenure in accordance with the University's Policy and Procedures for Granting Multi-annual Status to Tenure Track Academic Staff

    Salary Scale: Lecturer €52,187 - €83,038 p.a.

    Salary Scale: Lecturer below the bar €39,118 - €53,782 p.a.

    Further information for applicants and application material is available online.

    The closing date for receipt of applications is Monday, 3rd December 2018.

    Applications must be completed online before 12 noon, Irish Standard Time on the closing date.

    Please email erecruitment@ul.ie if you experience any difficulties

    Applications are welcome from suitably qualified candidates.

    The University of Limerick holds a Bronze Athena SWAN award in recognition of our commitment to advancing equality in higher education. The University is an equal opportunities employer and is committed to selection on merit welcoming applicants from all sections of the community. The University has a range of initiatives to support a family friendly working environment, including flexible working.

    “The University of Limerick has implemented a “Smoke and Vape Free Campus Policy”. Smoking and vaping in all forms is prohibited.”

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  • Geo-Sci-Info

    OFFICIAL WEBSITE APPLY
    The Siri Search team is creating groundbreaking technology for algorithmic search, machine learning, NLP, and artificial intelligence. The features we build are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for.

    Siri’s universal search engine powers search features across a variety of Apple products, including Siri, Spotlight, Safari, Messages and Lookup. We work with one of the most exciting high performance computing environments, with petabytes of data, millions of queries per second, and have an opportunity to imagine and build products that delight our customers every single day.

    Now imagine what you could do at Apple?

    Key Qualifications
    Industry experience in a Data Science, Machine Learning or Natural Language Processing
    Ability to illuminate complex problems with data analysis
    Proven product success derived from research and analysis results
    Familiarity with Hadoop, Mapreduce and similar technologies
    Experience with machine learning models and systems like Tensorflow
    Programming language like Python or Go
    Experience with Search/Information Retrieval is a plus
    Good communication with internal and external teams
    Fluency in at least one of French, German, Italian, Spanish
    Fluency in English

    Description

    This role is part of a growing team so we're you will have the opportunity to work in a few areas including Data Science, ML and NLP. Collaborating with team members in Europe and around the world you can work on many areas including:

    Perform data mining to support new features - Analyze large datasets to glean actionable insights - Design classifiers and ranking algorithms - Perform language processing and query analysis - Perform ad-hoc statistical analysis - Present results of analysis to team and leadership across Apple - Craft metrics to measure the success of the service

    If this is you, we'd love to hear from you.

    Education & Experience

    MS or Ph.D. in Data Mining, Machine Learning, Statistics, Natural Language Processing (in European languages), Operations Research or related field

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  • Geo-Sci-Info

    Capture du 2018-11-26 23-17-57.png

    OFFICIAL WEBSITE
    DOWNLOAD POSTER

    Bienvenue

    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 (http://www.peiresc.org).

    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.

    Cours
    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)

    Inscriptions
    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
    Présidence

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

    Direction Scientifique

    Contacts
    Pour toute demande de renseignement, veuillez nous envoyer un mèl à
    peyresq19_l AT gretsi.fr

    Acces
    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 (http://www.itaxis.fr/annot-04240.htm).
    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.

    Sponsors
    Capture du 2018-11-26 23-33-01.png

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  • Geo-Sci-Info

    Capture du 2018-11-25 13-52-07.png

    OFFICIAL WEBSITE

    The Numerical Tours of Data Sciences

    The Numerical Tours of Data Sciences, by Gabriel Peyré, gather Matlab, Python, Julia and R experiments to explore modern mathematical data sciences. They cover data sciences in a broad sense, including imaging, machine learning, computer vision and computer graphics. It showcases application of numerical and mathematical methods such as convex optimization, PDEs, optimal transport, inverse problems, sparsity, etc. The tours are complemented by slides of courses detailing the theory and the algorithms.

    Numerical Tours now in R
    Link - 35 R tours available
    Posted by Gabriel Peyré on February 26, 2018

    Numerical Tours on Machine Learning
    Link - 4 new Matlab and Python tours
    Posted by Gabriel Peyré on August 11, 2017

    Numerical Tours now in Julia
    Link - 30 Julia tours available
    Posted by Gabriel Peyré on August 5, 2017

    Numerical Tours now in Python
    Link - 30 Python tours available
    Posted by Gabriel Peyré on September 17, 2016

    New Python Tours
    Optimization by Laurent Condat
    Posted by Gabriel Peyré on June 14, 2016

    Capture du 2018-11-25 13-59-26.png
    Numerical Tours now in R
    OFFICIAL WEBPAGE
    The R tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

    Basics

    Wavelets

    Approximation, Coding and Compression

    Denoising

    Inverse Problems

    Optimization

    Machine Learning

    Shapes

    Audio Processing

    Computer Graphics

    Mesh Parameterization and Deformation

    Geodesic Processing

    Optimal Transport

    Capture du 2018-11-25 14-31-08.png

    OFFICIAL WEBPAGE

    hese are the Python tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

    Basics

    Wavelets

    Approximation, Coding and Compression

    Denoising

    Inverse Problems

    Optimization

    Shapes

    Audio Processing

    Computer Graphics

    Mesh Parameterization and Deformation

    Geodesic Processing

    Optimal Transport

    Machine Learning

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  • Geo-Sci-Info

    Postdoctoral position in Computer Vision & Machine Learning - [ Postdoc ] BC 75470
    Workplace: Genova, IIT, Italy
    Added on: 26/06/2018 - Expires on 31/12/2018

    The Pattern Analysis and Computer Vision Research Line (PAVIS) at IIT in Genova is looking for a highly qualified post doc with a strong background in Computer Vision, Pattern Recognition and Machine Learning, with particular emphasis on recognition, video analysis, behavior understanding, and prediction. As the activities may be carried out in collaboration with other IIT research units, the previous multidisciplinary experience is an added value which will be duly considered.
    The main mission of PAVIS is to design and develop innovative image- and video-based intelligent systems, characterized by the use of highly functional smart sensors and advanced data analytics features. PAVIS also plays an active role in supporting the other IIT research units providing scientists in Neuroscience, Nanophysics and other IIT departments/centers with ad-hoc solutions.
    To this end, the group is involved in activities concerning computer vision and pattern recognition, machine learning, multimodal\multimedia data analysis and sensor fusion, and embedded computer vision systems. The lab will pursue this goal by working collaboratively and in cooperation with external private and public partners.

    In particular, this call aims at consolidating PAVIS expertise in the video surveillance area and especially on action/activity recognition and scene understanding from video sequences and other sensory modalities.
    In particular, the following topics are of interest:
    Analysis of static and dynamic scenes.
    Recognition (objects, scenes, actions, events, etc.) and reconstruction.
    Behavior Analysis and Activity Recognition (individuals, groups, crowd).
    Prediction of intentions.
    Domain Adaptation.
    Multimodal data analysis
    Zero-shot Learning

    From the methodological standpoint, the ideal candidate should be familiar with one or more of the following subjects (it’s not an exhaustive list): Deep Learning, Graphical Models, Topic Models, Representation/Feature Learning, Sparse and Dictionary Learning, Clustering, Kernel methods, Manifold Learning and Statistical and Probabilistic Models in general.
    Candidates to this position have a Ph.D. in Computer Vision, Machine Learning, Pattern Recognition or related areas. Research experience and qualification in computer vision and pattern recognition/machine learning are clearly a must and evidence of top quality research on the above-specified areas in the form of published papers in top conferences/journals and/or patents is mandatory.
    Moreover, experience in the preparation and management of research proposals (EU, US, national) and industrial research projects, a few years of postdoc experience, either in academia or in an industrial lab, will also be duly considered. The winning candidate will also be asked to contribute to setting up new (funding) project proposals and will participate in funding activities.
    He/she is also expected to publish his/her research results in leading international journals and conferences, supervise Ph.D. candidates and collaborate with other scientists, also with different expertise.
    Salary will be commensurate to qualification and experience and in line with international standards.
    Further details and informal inquiries can be made by email to pavis@iit.it quoting PAVIS-PD 75470 as the reference number.
    Please send your completed application forms by December 31, 2018. The application must include: a curriculum listing all publications (possibly including a pdf of your most representative publications), a research statement describing your previous research experience and outlining its relevance to the above topics and the name of 2 referees by email to pavis@iit.it quoting PAVIS-PD 75470 as the reference number.

    IIT was established in 2003 and successfully created a large-scale infrastructure in Genova, a network of 10-state-of-the-art laboratories countrywide and recruited an international staff of about 1100 people from more than 50 countries. IIT's research endeavor focuses on high-tech and innovation, representing the forefront of technology with possible applications from medicine to industry, computer science, robotics, life sciences, and nanobiotechnologies.
    We inform you that the information you provide will be used solely for the purposes of evaluating and selecting professional profiles in order to meet the requirements of Istituto Italiano di Tecnologia.
    Your data will be processed by Istituto Italiano di Tecnologia, based in Genoa, Via Morego 30, acting as Data Controller, in compliance with the rules on protection of personal data, including those related to data security.
    Please also note that, pursuant to articles 15 et. seq. of European Regulation no. 679/2016 (General Data Protection Regulation), you may exercise your rights at any time by contacting the Data Protection Officer (phone +39 010 71781 - email: dpo@iit.it).
    Istituto Italiano di Tecnologia is an Equal Opportunity Employer that actively seeks diversity in the workforce.

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-45-27.png
    Venue
    ENAC, Toulouse (France)
    7, avenue Edouard BelinCS 54005
    31055 Toulouse Cedex 4
    France
    GPS ccordinates GPS : 43.565156, 1.479281

    https://goo.gl/maps/D6KQBVTdVcT2

    Ecole Nationale de l'Aviation Civile
    7, avenue Edouard Belin CS 54005
    31055 Toulouse Cedex 4
    http://www.enac.fr/en https://www.youtube.com/watch?v=DwkJKuYuCXY

    Access

    Shuttle from Toulouse-Blagnac Airport

    Free "Airport" shuttle, reserved for ENAC students, teachers and speakers according to availability.
    Boarding on presentation of notification or student / trainee card.

    By public transport

    All information, maps and directions for public transportation in Toulouse are available on Tisseo website ///

    • N°68 - to La terrasse / Métro Ramonville
    • N°78 - to Université Paul Sabatier / Lycée St Orens
    • N°37 - to Jolimont / Métro Ramonville

    Subway - Line B

    • Get off at Faculté de pharmacieFaculty and take bus N°78 to Lycée St Orens
    • Get off at Ramonville-Saint-Agne and take bus N°68 to "La terrasse"

    Subway - Line A

    Get off at Jolimont and take bus N°37 to Ramonville Metro or bus N°68

    By car
    Take outer ring road (towards "Montpellier"), then follow "Toulouse center / Foix / Tarbes" (green sign). Exit N°20, follow "Complexe scientifique Rangueil".
    GPS : 43.565156, 1.479281

    planenac.png

    picenac1.jpg
    picenac2.jpg
    picenac4.jpg
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    Social events
    Welcome Coktail
    will take place 27th August at "Mairie de Toulouse Palace” gsi2019-se1.jpg

    Gala Diner
    will take place 28th August at Hôtel-Dieu Saint-Jacques in Salle Des Colonnes. »
    gsi2019-se3.jpg gsi2019-se2.jpg gsi2019-se4.jpg

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-45-27.png

    Conference Co-chairs:

    Frank Nielsen- Sony CSL, Japan, Ecole Polytechnique, France Frank.png
    Frédéric Barbaresco - President of SEE ISIC Club Ingénierie des Systèmes d’Information et de Communications, Thales Land & Air Systems, Limours, France Frédéric.png

    Local organizing committee:

    Scientific Committee (To be consolidated)

    • Bijan Afsari – Johns Hopkins University
    • Pierre-Antoine Absil – Université Catholique de Louvain
    • Stephanie Allasonnière – Paris-Diderot University
    • Jesus Angulo – Mines ParisTech
    • Marc Arnaudon – Bordeaux University
    • John Armstrong – King’s College London
    • Anne Auger – Ecole Polytechnique
    • Nihat Ay – Max Planck Institute
    • Roger Balian – CEA
    • Frédéric Barbaresco – Thales Land & Air Systems
    • Pierre Baudot – MEDIAN
    • Daniel Bennequin – Paris-Diderot University
    • Joel Bensoam – IRCAM
    • Yannick Berthoumieu – Bordeaux University
    • Jeremie Bigot – Bordeaux University
    • Silvere Bonnabel – Mines ParisTech
    • Michel Broniatowski – Sorbonne University
    • Michel Boyom – Montpellier University
    • Marius Buliga – Simion Stoilow Institute of Mathematics of the Romanian Academy
    • Laurent Cohen – Paris Dauphine University
    • Ana Bela Cruzeiro – Universidade de Lisboa
    • Remco Duits - Eindhoven University of Technology
    • Stanley Durrleman – INRIA
    • Alfred Galichon – New York University
    • Fabrice Gamboa – Institut Mathématique de Toulouse
    • Jean-Pierre Gazeau – Paris Diderot University
    • François Gay-Balmaz – ENS Ulm
    • Mark Girolami – Imperial College London
    • Susan Holmes – Stanford University
    • Jérémie J. Jakubowicz – Telecom SudParis
    • Jean Lerbet – Evry University
    • Nicolas Le Bihan – Grenoble University
    • Luigi Malago – Romanian Institute of Science and Technology
    • Jonathan Manton - The University of Melbourne
    • Gaetan Marceau-Caron – MILA R&D and Tech Transfer
    • Matilde Marcolli - CALTECH
    • Jean-François Marcotorchino – Sorbonne University
    • Charles-Michel Marle – Sorbonne University
    • Hiroshi Matsuzoe - Nagoya Institute of Technology
    • Jean-Marie Mirebeau – Paris Orsay University
    • Ali Mohammad-Djafari – Centrale Supelec
    • Antonio Mucherino - IRISA, University of Rennes 1
    • Florence Nicol - ENAC
    • Frank Nielsen - Ecole Polytechnique, Paris-Saclay University
    • Richard Nock - Université Antilles-Guyane
    • Yann Ollivier – FACEBOOK FAIR Paris
    • Steve Oudot – INRIA
    • Pierre Pansu – Paris-Saclay University
    • Xavier Pennec - INRIA
    • Giovanni Pistone – CarloAlberto University
    • Stephane Puechmorel - ENAC
    • Olivier Rioul – Telecom ParisTech
    • Gery de Saxcé – Lille University
    • Salem Said – Bordeaux University
    • Rodolphe Sepulchre – Liège University
    • Olivier Schwander – Sorbonne University
    • Stefan Sommer – Copenhagen University
    • Dominique Spehner – Grenoble University
    • Alain Trouvé – Ecole Normale Supérieure Paris-Saclay
    • Geert Verdoolaege – Ghent University
    • Rene Vidal – Johns Hopkins University
    • Jun Zhang – University of Michigan, Ann Arbor

    Contact
    Please use the form below to contact them electronically.

    • ALIDOR Valérie
      SEE – Congress
      Société de l'Electricité, de l'Electronique
      et des Technologies de l'Information
      et de la Communication
      17 rue de l'Amiral Hamelin
      75783 Paris Cedex16
      Phone: +33 (0)1 56 90 37 02
    • Frank NIELSEN
      Professor (PhD 1996, Habilitation 2006)
      Campus de l'École Polytechnique
      1, rue Honoré d'Estienne d'Orves
      Bâtiment Alan Turing
      91120 Palaiseau
      Phone: +33 (0)1 77 57 80 70
      (Office 2028)

    • Frédéric BARBARESCO
      THALES AIR SYSTEMS
      Technical Directorate
      Advanced Department Developments
      Voie Pierre-Gilles de Gennes, F-91470 Limours, FRANCE
      Phone: +33 (0)6 30 07 14 19

    Capture du 2018-11-18 21-57-34.png

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-45-27.png
    PROBABILITY & GEOMETRY OF CHANCE (invention by Pierre de FERMAT and Blaise PASCAL in letters of their correspondence)
    Seminal Blaise Pascal paper – ALEAE GEOMETRIA, De compositione aleae in ludis ipsi subjectis, Celeberrimae matheseos Academiae Parisiensi – 1654. « ... inviter les savants géomètres à traiter nos problèmes avec le soucis de la commodité et de l’agrément : qu’ils écartent tout ce qui n’a rien à voir avec la pénétration de l’esprit, seule qualité dont nous faisons grand cas et que nous nous sommes proposé d’éprouver et de couronner » Blaise Pascal – Deuxième Lettre sur la roulette, Paris, 19 Juillet 1658

    DOWNLOAD THE CALL FOR PAPER - PDF

    Submission dates

    • Deadline for 8 pages SPRINGER LNCS format: 18th of February 2019
    • Notification of acceptance: 22nd of April 2019
    • Final paper submission: 15th of June 2019

    Submission page : https://easychair.org/conferences/?conf=gsi2019.

    GSI2019 Call for Papers
    GSI2019 Word splnproc1703
    GSI2019 Latex-llncs2e template
    GSI2019 Copyright Form Springer

    Capture du 2018-11-18 21-57-34.png

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-45-27.png
    OFFICIAL WEBSITE

    DOWNLOAD POSTER

    4th conference on Geometric Science of Information - 27 August 2019 - 29 August 2019 ENAC, Toulouse (France)

    As for GSI’13, GSI’15 and GSI’17, the objective of this SEE GSI’19 conference, hosted in Toulouse at ENAC, is to bring together pure/applied mathematicians and engineers, with common interest for Geometric tools and their applications for Information analysis.
    It emphasizes an active participation of young researchers to discuss emerging areas of collaborative research on “Geometric Science of Information and their Applications”.
    Current and ongoing uses of Information Geometry Manifolds in applied mathematics are the following: Advanced Signal/Image/Video Processing, Complex Data Modeling and Analysis, Information Ranking and Retrieval, Coding, Cognitive Systems, Optimal Control, Statistics on Manifolds, Topology/Machine/Deep Learning, Artificial Intelligence, Speech/sound recognition, natural language treatment, Big Data Analytics, Learning for Robotics, etc., which are substantially relevant for industry.
    The Conference will be therefore held in areas of topics of mutual interest with the aim to:

    • Provide an overview on the most recent state-of-the-art
    • Exchange mathematical information/knowledge/expertise in the area
    • Identify research areas/applications for future collaboration

    This conference will be an interdisciplinary event and will unify skills from Geometry, Probability and Information Theory. Proceedings are published in Springer's Lecture Note in Computer Science (LNCS) series. SPRINGER will sponsor Best paper Award GSI’19.
    Gala Diner will take place at Hôtel-Dieu Saint-Jacques in Salle Des Colonnes.

    Provisional topics of interests:

    • Probability on Riemannian Manifolds
    • Optimization on Manifold
    • Shape Space
    • Statistics on non-linear data
    • Lie Group Machine Learning
    • Harmonic Analysis on Lie Groups
    • Statistical Manifold & Hessian Information Geometry
    • Monotone Embedding in Information Geometry
    • Non-parametric Information Geometry
    • Computational Information Geometry
    • Divergence Geometry
    • Optimal Transport
    • Geometric Deep Learning
    • Geometry of Hamiltonian Monte Carlo
    • Information Topology
    • Geometric & (Poly)Symplectic Integrators
    • Geometric structures in thermodynamics and statistical physics
    • Contact Geometry & Hamiltonian Control
    • Geometric and structure preserving discretizations
    • Geometry of Quantum States
    • Geodesic Methods with Constraints
    • Probability Density Estimation & Sampling in High Dimension
    • Geometry of Graphs and Networks
    • Distance Geometry
    • Geometry of Tensor-Valued Data
    • Geometric Mechanics
    • Geometric Robotics & Learning
    • Geometry in Neuroscience & Cognitive Sciences

    A special session will deal with:

    • Geometric Science of Information Libraries (geomstats, pyRiemann , …)

    Important dates

    • Deadline for 8 pages SPRINGER LNCS format: 18th of February 2019
    • Notification of acceptance: 22nd of April 2019
    • Final paper submission: 15th of June 2019

    Provisional program of Invited Speakers:
    History Session: TBC on “Fermat, Pascal & the Geometry of Chance”, Frédéric Barbaresco & Michel Boyom “Tribute to Jean-Louis Koszul (who passed away in January 2018)”
    Invited Honorary speaker: TBC, Guest Honorary speaker: TBC, and 3 keynotes Speakers: TBC
    (TBC: To be Confirmed)

    Sponsors
    Capture du 2018-11-18 21-57-34.png

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    Information.jpg

    OFFICIAL WEBSITE

    Programme
    L’information
    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

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  • Geo-Sci-Info

    Capture du 2018-11-18 21-05-18.png

    OFFICIAL WEBSITE

    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.

    Speakers:

    • 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!

    Capture du 2018-11-18 21-10-24.png

    posted in From Stochastic Geometric Mechanics to Mass Transportation Problems read more
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