Geometry, Topology and Statistics in Data Sciences

Geometry, Topology and Statistics in Data Sciences

10-14 October - IHP, Paris

On one hand, modern data science makes use of Topological Data Analysis in a preliminary step to obtain structural information before processing supervised or unsupervised methods. On the other hand, when a priori knowledge of a Riemannian manifold containing the data is available, shape analysis proposes to adapt mathematical statistics tools to infer geometric and statistical properties.

Invited Speakers

• Dominique Attali (GIPSA-lab) - Reconstructing manifolds by weighted $\ell_1$-norm minimization
• Martin Bauer (Florida State University) - Elastic shape analysis of surfaces
• Omer Bobrowski (Technion Israel Institute of Technology) - Universality in random persistence diagrams
• Frédéric Barbaresco (Thales) - Symplectic foliation model of information geometry for statistics and learning on Lie groups
• Claire Brécheteau (University Rennes 2) - Approximating data with a union of ellipsoids and clustering
• Nicolas Charon (Johns Hopkins University) - Registration of shape graphs with partial matching constraints
• Herbert Edelsbrunner (Institute of Science and Technology Austria) - Chromatic Delaunay mosaics for chromatic point data
• Barbara Gris (Sorbonne University) - Defining data-driven deformation models
• Heather Harrington (Oxford University) - TBA
• Kathryn Hess (EPFL) - Morse-theoretic signal compression and reconstruction
• Irène Kaltenmark (Université de Paris) - Curves and surfaces. Partial matching in the space of varifolds.
• Eric Klassen (Florida State University) - The square root normal field and unbalanced optimal transport
• Johannes Krebs (KU Eichstaett) - On the law of the iterated logarithm and Bahadur representation in stochastic geometry
• Nina Miolane (UC Santa Barbara) - Geomstats: a Python package for Geometric Machine Learning
• Steve Oudot (Inria Paris Saclay) - Optimization in topological data analysis
• Victor Patrangenaru (Florida State University) - Geometry, topology and statistics on object spaces
• Stephen Preston (City University of New York) - Isometric immersions and the waving of flags
• Stefan Horst Sommer (University of Copenhagen) - Diffusions means in geometric statistics
• Katharine Turner (Australian National University) - TBA
• Yusu Wang (UC San Diego) - Weisfeiler-Lehman meets Gromov-Wasserstein
• Laurent Younes (Johns Hopkins University) - Stochastic gradient descent for large-scale LDDMM