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Shedule -

2022-T3 Geometry & Statistics in Data Sciences

Geometry, Topology and Statistics in Data Sciences

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

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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.

Program

Invited Speakers

  • Dominique Attali (GIPSA-lab)
    • Reconstructing manifolds by weighted $\ell_1$-norm minimization (video)
  • Martin Bauer (Florida State University) 
    • Elastic shape analysis of surfaces
  • Omer Bobrowski (Technion Israel Institute of Technology)
    • Universality in random persistence diagrams (video)
  • Frédéric Barbaresco (Thales)
    • Symplectic foliation model of information geometry for statistics and learning on Lie groups (video)
  • Claire Brécheteau (Ecole Centrale de Nantes)
    • Approximating data with a union of ellipsoids and clustering (video)
  • Nicolas Charon (Johns Hopkins University)
    • Registration of shape graphs with partial matching constraints (video)
  • Herbert Edelsbrunner (Institute of Science and Technology Austria)
    • Chromatic Delaunay mosaics for chromatic point data (video)
  • Barbara Gris (Sorbonne University)
    • Defining data-driven deformation models (video)
  • Joan Glaunès (Université Paris Cité)
    • Geometric methods for diffeomorphic registration, and the KeOps library (video)
  • Heather Harrington (Oxford University)
    • Shape of data in biology (video)
  • Kathryn Hess (EPFL)
    • Morse-theoretic signal compression and reconstruction (video)
  • Irène Kaltenmark (Université de Paris)
    • Curves and surfaces. Partial matching in the space of varifolds (video)
  • 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 (video)
  • Stefan Horst Sommer (University of Copenhagen)
    • Diffusions means in geometric statistics
  • Katharine Turner (Australian National University)
    • The extended persistent homology transform for manifolds with boundary 
  • Yusu Wang (UC San Diego)
    • Comparing graphs with node attributes: Wesifeiler-Lehman meets Gromov-Wasserstein
  • Laurent Younes (Johns Hopkins University)
    • Stochastic gradient descent for large-scale LDDMM (video1, video2)

 

Cocktail

For logistical reasons, to join the cocktail at Tour Zamansky, please ensure that you are registered to attend the workshop in person.

 

Lunches (Info handout)

  • Institut Henri Poincaré’s neighborhood has plenty of diverse food places.
    The two-hour lunch breaks give you enough time to eat in a restaurant, or to grab something to take away.