GSI 2021 : International Conference on Geometric Science of Information

  in Conferences   Posted on November 4, 2020

Conference Information

Submission Deadline Sunday 14 Feb 2021 Proceedings indexed by :
Conference Dates Jul 21, 2021 - Jul 23, 2021
Conference Address Paris, France
Conference & Submission Link https://www.see.asso.fr/en/GSI2021?screen_check=done&Width=1440&Height=900
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Impact Score 1.49
#Contributing Top Scientists 11
#Papers published by Top Scientists 14
Google Scholar H5-index 12
Number of Editions: 5
Guide2Research Overall Ranking: 678
Category Rankings
Computational Theory and Mathematics 61
Proceedings https://link.springer.com/conference/gsi

Conference 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:
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
Divergence Geometry
Optimal Transport
Geometric Deep Learning
Geometry of Hamiltonian Monte Carlo
Information Topology
Geometric & (Poly)Symplectic Integrators
Geometric structures in thermodynamics and statistical physics
Contact Geometry & Hamiltonian Control
Geometric and structure preserving discretizations
Geometry of Quantum States
Geodesic Methods with Constraints
Probability Density Estimation & Sampling in High Dimension
Geometry of Graphs and Networks
Distance Geometry
Geometry of Tensor-Valued Data
Geometric Mechanics
Geometric Robotics & Learning
Geometry in Neuroscience & Cognitive Sciences

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