Track / Overview

Topological Data Analysis (TDA) is a rapidly growing field with tremendous potential for improving machine learning pipelines, unboxing the most intricate deep networks, and creating new geometric features from data.

Since the knowledge barrier that protects TDA might scare some data scientists, the purpose of this track is not only to showcase TDA as a complementary tool to machine learning, but also to lower this barrier and make TDA more intuitive and accessible.

The speakers of this track are all renowned leaders in the topics they present; dynamical system analysis using TDA, analysis of deep networks with TDA, feature engineering via TDA, data visualisation and dimensionality reduction are the main topics discussed in this track.

Track / Speakers

Martin Jaggi

Professor, EPFL

Kathryn Hess Bellwald

Professor, EPFL

Marco Armenta

Postdoctoral fellow, University of Sherbrooke

Nicolas Berkouk

Post-Doctoral Researcher, EPFL

Elizabeth Munch

Assistant Professor, Michigan State University

Bryn Keller

Research Scientist, Intel Labs

Rickard Brüel-Gabrielsson


Shusen Liu

Research Scientist, Lawrence Livermore National Laboratory

Track / Co-organizers

Kathryn Hess Bellwald

Professor, EPFL

Matteo Caorsi

Chief Scientist, L2F SA

AMLD EPFL 2021 / Tracks & talks

AI & Physics

Alexey Melnikov, Giuseppe Carleo, Balaji Lakshminarayanan, Marylou Gabrié, Agnes Valenti, Bruno Loureiro, Gregor Kasieczka, Anna Dawid, Paolo Molignini, Roman Worschech, Diego Tapias, Aishik Ghosh, Frank Schäfer, Andrew Saxe

09:00-18:15 September 30Online

AI & Resilience in dynamic environments

Grégoire Caro, Ilya Feige, Lisa R. Goldberg, Jeffrey R. Bohn, Olga Fink

09:00-17:00 October 25Online

AI & Cities

09:00-17:00 December 02Forum Rolex, EPFL and online

AMLD / Global partners