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GSI'17 Geometric Science of Information

26 December 2016

Catégorie : Conférence internationale

GSI'17 Geometric Science of Information, Ecole des Mines de Paris, 7th-9th November 2017

As for GSI’13 and GSI’15, the objective of this SEE Conference GSI’17, hosted in Paris, 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 “Information Geometry Manifolds and Their Advanced 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, Machine & Deep Learning, Artificial Intelligence, Speech/sound recognition, natural language treatment, Big Data Analytics, etc., which are also substantially relevant for industry.

The Conference will be therefore held in areas of priority/focused themes and 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
  • Identify academic & industry labs expertise for further collaboration



GSI'17 Geometric Science of Information, 7th-9th November 2017, Ecole des Mines de Paris


Call for Papers

Main deadlines are the following:​

Deadline for 8 pages LNCS format: 24th of April 2017
Notification of acceptance: 12th of June 2017
Final paper submission: 31st of July 2017

Author instruction:

Submission link:

This conference will be an interdisciplinary event and will unify skills from Geometry, Probability and Information Theory. The conference proceedings are published in Springer's Lecture Note in Computer Science (LNCS) series.

The following Special Sessions have been identified but will not be limited to:

  • Statistics on non-linear data
  • Shape Space
  • Optimal Transport & Applications I (Data Science and Economics)
  • Optimal Transport & Applications II (Signal and Image Processing)
  • Topology and statistical learning
  • Statistical Manifold & Hessian Information Geometry
  • Monotone Embedding in Information Geometry
  • Information Structure in Neuroscience
  • Geometric Robotics & Tracking
  • Geometric Mechanics & Robotics
  • Stochastic Geometric Mechanics & Lie Group Thermodynamics
  • Probability on Riemannian Manifolds
  • Divergence Geometry
  • Geometric Deep Learning
  • First and second-order Optimization on Statistical Manifolds
  • Non-parametric Information Geometry
  • Geometry of quantum states
  • Optimization on Manifold
  • Computational Information Geometry
  • Probability Density Estimation
  • Geometry of Tensor-Valued Data
  • Geometry and Inverse Problems
  • Geometry in Vision, Learning and Dynamical Systems
  • Lie Groups and Wavelets
  • Geometry of metric measure spaces
  • Geometry and Telecom
  • Geodesic Methods with Constraints
  • Applications of Distance Geometry

Keynote speakers:

3 keynote speakers’ talks will open each day (Prof. A. Trouvé, B. Tumpach & M. Girolami). An Invited Honorary speaker (Prof. J.M. Bismut) will give a talk at the end of 1st day and a Guest Honorary speaker (Prof. D. Bennequin) will close the conference.

  • Invited Honorary Speaker:
    • Jean-Michel Bismut (Paris-Saclay University) - The hypoelliptic Laplacian
  • Guest Honorary Speaker:
    • Daniel Bennequin (Paris Diderot University) - Geometry and Vestibular Information
  • Keynote Speakers:
    • Alain Trouvé (ENS Cachan) - Hamiltonian Modeling for Shape Evolution and Statistical Modeling of Shapes Variability
    • Barbara Tumpach (Lille University) - Riemannian Metrics on Shape Spaces of Curves and Surfaces
    • Mark Girolami (Imperial College London) - Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods