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 state-of-the-art

• Exchange mathematical information/knowledge/expertise in the area

• Identify research areas/applications for future collaboration

**Provisional topics of interests:**

- Geometric Deep Learning (ELLIS session)
- 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
- 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 Tensor-Valued 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**

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 state-of-the-art

• Exchange mathematical information/knowledge/expertise in the area

• Identify research areas/applications for future collaboration

**Provisional topics of interests:**

- Geometric Deep Learning (ELLIS session)
- 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
- 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 Tensor-Valued 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**