Track / Overview

Topological Data Analysis (TDA) and Topological Machine Learning (TML) comprise a set of powerful techniques whose ability to extract robust geometric information has led to novel insights in the analysis of complex data.

Topology is concerned with understanding the global shape and structure of objects. When applied to data, topological methods provide a natural complement to conventional machine learning approaches, which tend to rely on local properties of the data. The main strengths of TDA/TML include their robustness to noise and ability to succinctly capture multi-scale behaviour.

Topological techniques have found great success in domains ranging from network science to drug and materials design, visualisation and dimensionality reduction, neuroscience, and time series analysis. Recently, connections between topology and deep learning have been explored, revealing promising avenues into regularization, interpretability, and protection from adversarial attacks.

This track will provide an accessible introduction to the use of topology in data science and machine learning. Via a selection of use cases from experts in the field, this track will showcase the benefits of integrating TDA and TML into traditional data science workflows.

Track / Schedule

Topological adventures in machine learning

With Kathryn Hess Bellwald

Learning linear representation of persistence diagrams: some mathematical aspects and applications

With Frédéric Chazal

Geometric and Topological Data Analysis for Materials Discovery

With Vitaliy Kurlin

Introduction to topology-based graph classification

With Bastian Rieck


Topology and Language

With Leland McInnes

Applications of Computational Topology to Time Series Analysis

With Nicole Sanderson

Panel discussion with Q&A session

With Frédéric Chazal, Nicole Sanderson, Bastian Rieck, Umberto Lupo, Vitaliy Kurlin, Leland McInnes & Kathryn Hess Bellwald

Closing remarks

With Umberto Lupo

Track / Speakers

Umberto Lupo

Research Scientist, L2F

Vitaliy Kurlin

Senior Lecturer, University of Liverpool

Leland McInnes

Researcher, Tutte Institute

Kathryn Hess Bellwald

Professor, EPFL

Nicole Sanderson

Postdoctoral Scholar, LBNL

Bastian Rieck

Senior Assistant, ETH Zurich

Frédéric Chazal

Research Director, Inria

Track / Co-organizers

Matteo Caorsi

Chief Scientist, L2F SA

Umberto Lupo

Research Scientist, L2F

Kathryn Hess Bellwald

Professor, EPFL

Thomas Boys

The voice behind Giotto, L2F

AMLD EPFL 2020 / Tracks & talks

AI & Nutrition

Marinka Zitnik, Marcel Salathé, Fabio Mainardi, Tome Eftimov, Barbara Koroušić Seljak, Nives Ogrinc, Aleksandra Kovachev

13:30-17:00 January 282A

AI & Policy

Joanna Bryson, Sofia Olhede, Emanuele Baldacci, Sabrina Kirrane, Bruno Lepri, Dennis Diefenbach, Ioannis Kaloskampis, Benoît Otjacques, Steve MacFeely, Christina Corbane

13:30-17:30 January 272A

Challenge Track

Danny Lange, Sunil Mallya, Marcel Salathé, Florian Laurent, Erik Nygren, Sharada Mohanty, Parth Kothari, Navid Rekabsaz, Wilhelmina Welsch, Ewan Oglethorpe, Nicholas Jones, Gokula Krishnan, Jeremy Watson, Andrew Melnik

13:30-17:00 January 284A

AMLD / Global partners