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 / Co-organizers

Kathryn Hess Bellwald

Professor, EPFL

Matteo Caorsi

Chief Scientist, L2F SA

AMLD EPFL 2021 / Tracks & talks

AI & Democracy

Robert West, Roy Gava, Victor Kristof, Steven Eichenberger, Alexandra Siegel, Lucas Leemann, Rayid Ghani, Sophie Achermann, Alexander Immer, Jacques Savoy, Oana Goga, Christine Choirat, Arianna Ornaghi, Irio Musskopf

10:00-18:00 January 25

AI & Food and Nutrition

09:00-17:00 March 01

Clinical Machine Learning

09:00-17:00 March 18

AMLD / Global partners