Track / Overview

Machine learning (ML) approaches are becoming ubiquitous in the broad fields of Life Sciences and Chemistry. The impact that ML approaches have had in these fields were unforeseen by the community at large and we are yet to understand the full impact that they will have in the mid to long term. Many of the subjects of study in Life Sciences and chemistry lie within the molecular scale, and specifically at this scale ML approaches have made incredible progress to solve long-standing problems in the field. It is clear that the novelty of the approach will fade, which will raise the standards of performance for the developed algorithms and the quest for interpretability of the “black boxes”.

In our track, we will attempt to gather some of the key players on the use of machine learning to deal with molecular entities and those that are looking into new challenges and problems that lie ahead. This will be the second year we are organizing the track which we believed raise a fair amount of interest within the audience last year.

Track / Schedule

Machine Learning-based Design of Proteins and Small Molecules

With Jennifer Listgarten

Conditional Generation of Molecules from Disentangled Representations

With Amina Mollaysa

An Autoencoder for 3D Geometries of Atomic Structures with Euclidean Neural Networks

With Tess E. Smidt

Physics-inspired Machine Learning for Materials Discovery

With Michele Ceriotti


AlphaFold: Improved protein structure prediction using potentials from deep learning

With Andrew Senior

Data Driven Discovery of Functional Molecular Co-Crystals

With Aikaterini Vriza

Knitting together synthetic biology, machine learning and robotics

With Katya Putintseva

Track / Speakers

Jennifer Listgarten

Professor, EECS, UC Berkeley

Andrew Senior

Research Scientist, DeepMind

Tess E. Smidt

Postdoctoral Fellow, Lawrence Berkeley National Laboratory

Amina Mollaysa

PhD Student, University of Geneva

Aikaterini Vriza

PhD Student, University of Liverpool

Michele Ceriotti

Assistant Professor, EPFL

Katya Putintseva

Data Scientist, LabGenius

Kostiantyn Lapchevskyi

Student, Ukrainian Catholic University

Track / Co-organizers

Philippe Schwaller

PhD Student, IBM Research Zurich & University of Bern

Bruno Correia

Tenure Track Assistant Professor, EPFL

Joppe Geluykens

Research Scientist Intern, IBM Research

Freyr Sverrisson

PhD Student, EPFL

Pablo Gainza Cirauqui

Postdoctoral Researcher, Bioengineering, EPFL

AMLD EPFL 2020 / Tracks & talks

AI & Climate Change

Lynn Kaack, Nikola Milojevic-Dupont, Nicholas Jones, Felix Creutzig, Buffy Price, Slava Jankin, Olivier Corradi, Liam F. Beiser-McGrath, Marius Zumwald, Eniko Székely, Max Callaghan, Soon Hoe Lim, Mohamed Kafsi, Daniel de Barros Soares, Matthias Meyer, Chris Heinrich, Emmanouil Thrampoulidis, Marta Gonzalez, Kristina Orehounig, David Dao, Bibek Paudel

13:30-17:00 January 2709:00-12:30 January 285ABC

AI & Humanitarian Action

Neil Davison, Max Tegmark, Carmela Troncoso, Alessandro Mantelero, Michela D'Onofrio, Francois Fleuret, Amina Chebira, John C. Havens, Marc Brockschmidt, Helen Toner, Dustin Lewis, Subhashis Banerjee, Rebeca Moreno Jimenez, Netta Goussac, Volkan Cevher, Anika Schumann, Nadia Marsan, Massimo Marelli, Anja Kaspersen

09:00-17:00 January 283A

AI & Cities 2020

Konstantin Klemmer, Shin Alexandre Koseki, Eun-Kyeong Kim, Nicholas Jones, Kamil Kaczmarek, Kiran Zahra, Roger Fischer, Doori Oh, Ran Goldblatt, Martí Bosch, Roman Prokofyev, Dmitry Kudinov, Camille Lechot, Ellie Cosgrave, Javier Pérez Trufero, Layik Hama, Hoda Allahbakhshi, Marta Gonzalez, Valery Fischer, Emmanouil Tranos, Jens Kandt, Yussuf Said Yussuf, Nyalleng Moorosi, Nick Lucius

09:00-17:00 January 281BC

AMLD / Global partners