GSI2021  Presentation

As for GSI’13, GSI’15, GSI’17 and GSI’19, the objective of this SEE GSI’21 conference, 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 “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 stateoftheart
• Exchange mathematical information/knowledge/expertise in the area
• Identify research areas/applications for future collaborationProvisional topics of interests:
 Geometric Deep Learning (ELLIS session)
 Probability on Riemannian Manifolds
 Optimization on Manifold
 Shape Space & Statistics on nonlinear data
 Lie Group Machine Learning
 Harmonic Analysis on Lie Groups
 Statistical Manifold & Hessian Information Geometry
 Monotone Embedding in Information Geometry
 Nonparametric Information Geometry
 Computational Information Geometry
 Distance and Divergence Geometries
 Divergence Statistics
 Optimal Transport & Learning
 Geometry of Hamiltonian Monte Carlo
 Statistics, Information & Topology
 Graph Hyperbolic Embedding & Learning
 Inverse problems: Bayesian and Machine Learning interaction
 Integrable Systems & Information Geometry
 Geometric structures in thermodynamics and statistical physics
 Contact Geometry & Hamiltonian Control
 Geometric and structure preserving discretizations
 Geometric & Symplectic Methods for Quantum Systems
 Geometry of Quantum States
 Geodesic Methods with Constraints
 Probability Density Estimation & Sampling in High Dimension
 Geometry of TensorValued Data
 Geometric Mechanics
 Geometric Robotics & Learning
 Topological and geometrical structures in neurosciences
A special session will deal with:
 Geometric Structures Coding & Learning Libraries (geomstats, pyRiemann , Pot…)
Advanced information on article submission and publication
As for previous editions, GSI’21 Proceedings will be published in SPRINGER LNCS. See GSI’19 Proceedings
8 pages SPRINGER LNCS format is required for Initial paper submission.
A detailed call for contributions will be published shortly.
CALL FOR PAPERS