About

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.

 It emphasises an 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. The conference proceedings, are published in Springer's Lecture Notes in Computer Science (LNCS) series.

- 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

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