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)
• Martin Bauer (Florida State University)
• Omer Bobrowski (Technion Israel Institute of Technology)
• Claire Brécheteau (University Rennes 2)
• Nicolas Charon (Johns Hopkins University)
• Herbert Edelsbrunner (Institute of Science and Technology Austria)
• Barbara Gris (Sorbonne University)
• Heather Harrington (Oxford University)
• Kathryn Hess (EPFL)
• Eric Klassen (Florida State University)
• Johannes Krebs (KU Eichstaett)
• Nina Miolane (UC Santa Barbara)
• Stephen Preston (City University of New York)
• Stefan Horst Sommer (University of Copenhagen)
• Katharine Turner (Australian National University)
• Yusu Wang (UC San Diego)
• Laurent Younes (Johns Hopkins University)