The objective of this SEE Conference hosted by MINES ParisTech, is to bring together pure/applied mathematicians and engineers, with common interest for Geometric tools and their applications for Information analysis, with active participation of young researchers for deliberating 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 Learning, Speech/sound recognition, natural language treatment, etc., which are also substantially relevant for the industry.

The Conference will be therefore being held in areas of priority/focused themes and topics of mutual interest with a mandate to:

Provide an overview on the most recent state-of-the-art
Exchange of mathematical information/knowledge/expertise in the area
Identification of research areas/applications for future collaboration
Identification of academic & industry labs expertise for further collaboration

This conference will be an interdisciplinary event and will federate skills from Geometry, Probability and Information Theory to address the following topics among others

Computational Information Geometry
Hessian/Symplectic Information Geometry
Optimization on Matrix Manifolds
Probability on Manifolds
Optimal Transport Geometry
Divergence Geometry & Ancillarity
Machine/Manifold/Topology Learning
Tensor-Valued Mathematical Morphology
Differential Geometry in Signal Processing
Geometry of Audio Processing
Geometry for Inverse Problems
Shape Spaces: Geometry and Statistic
Geometry of Shape Variability
Relational Metric
Discrete Metric Spaces