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
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 Saxe09:00-18:15 September 30Online
AI & Resilience in dynamic environments
Grégoire Caro, Ilya Feige, Lisa R. Goldberg, Jeffrey R. Bohn, Olga Fink09:00-17:00 October 25Online