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Shedule -

Centre Émile Borel, Workshop & courses

Geometry, Topology and Statistics in Data Sciences

Institut Henri Poincaré
Amphithéâtre Hermite
11 rue Pierre et Marie Curie 75005 Paris

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