Track / Overview

Machine learning has had a substantial impact on physics in recent years, where problems spanning all scales of the universe, from the identification of exoplanets to the detection of anomalies in particle collisions and the prediction of extreme weather events, have seen critical improvements due to applications of deep learning methods. These advances have been made possible through the permeation of recent machine learning advances in the physics community, creating unique insights through the combination of machine learning expertise and physics domain knowledge.

The AI & Physics track will aim to bring together experts working at the interface between machine learning and physics, as well as leading researchers from both the machine learning and physics communities. Machine learning has seen a surge of interest in physics, with techniques ranging from computer vision, natural language processing, generative modelling, and reinforcement learning leading to new methods to solve long-standing problems in the classification, simulation and analysis of physical systems. Areas of particular interest for this track include topics of relevance for the Large Hadron Collider at CERN, applications of machine learning in lattice QCD and cosmology, as well as studies of deep learning models through tools originating from statistical and mathematical physics.

The purpose of this track is to highlight important recent breakthroughs in applications of machine learning in physics, and provide an opportunity to discuss promising new directions in this area, through a combination of diverse expert presentations and a panel discussion. 

Track / Schedule


With Frédéric Dreyer

Neural simulation-based inference

With Gilles Louppe

Simulating the Universe with Machine Learning

With Shirley Ho

Learning molecular models from simulation and experimental data

With Cecilia Clementi

Tracking High Energy Particles with Deep Learning

With Jean-Roch Vlimant


Data ex Machina: Machine Learning with Public Collider Data

With Eric Metodiev

Predicting phase transitions in many-body physics

With Eliska Greplova

Exploring string theory solutions with reinforcement learning

With Fabian Ruehle

Statistical physics for machine learning

With Lenka Zdeborová

WIMPs or else? Using Machine Learning for Dark Matter Detection

With Charanjit Kaur

Panel discussion

With Maurizio Pierini, Lenka Zdeborová, Danilo Jimenez Rezende, Shirley Ho & Gilles Louppe

Generative Models and Symmetries

With Danilo Jimenez Rezende

Machine Learning in Physics and Beyond: experience at CERN openlab

With Sofia Vallecorsa

Deep learning driven model discovery in physics

With Remy Kusters


Probabilistic Inference in Simulators

With Atılım Güneş Baydin

Deep learning from physics to financial services

With Jeremie Abiteboul

A deep neural network for simultaneous estimation of b quark energy and resolution for the CMS experiment

With Nadezda Chernyavskaya

Can we optimize the operation of CERN's Large Hadron Collider with Machine Learning techniques?

With Loic Coyle

Track / Speakers

Frédéric Dreyer

Postdoctoral Researcher, University of Oxford

Maurizio Pierini

Research Staff, CERN

Lenka Zdeborová

Professor, EPFL

Gilles Louppe

Professor, University of Liège

Atılım Güneş Baydin

Postdoctoral Researcher, University of Oxford

Cecilia Clementi

Professor, Rice University

Eric Metodiev

PhD Candidate, MIT

Fabian Ruehle

Fellow, CERN

Jean-Roch Vlimant

Assistant Scientist, California Institute of Technology

Charanjit Kaur

Postdoctoral Researcher, University of Sussex

Loic Coyle

Student, EPFL & CERN

Nadezda Chernyavskaya

Research Scientist & Data Scientist, ETH Zurich and CERN CMS Collaboration

Remy Kusters

Research Fellow, CRI Paris

Sofia Vallecorsa

Physicist, CERN

Danilo Jimenez Rezende

Senior Staff Research Scientist, Google DeepMind

Shirley Ho

Group Leader & Professor at Flatiron Institute

Jeremie Abiteboul

Chief Data Science Officer, DreamQuark

Eliska Greplova

Postdoctoral Researcher, ETH Zurich

Balazs Kegl

Head of AI Research, Huawei France

Track / Co-organizers

Frédéric Dreyer

Postdoctoral Researcher, University of Oxford

Tobias Golling

Professor, University of Geneva

Slava Voloshynovskiy

Professor, University of Geneva

Stefano Carrazza

Researcher, University of Milan

Maurizio Pierini

Research Staff, CERN

David Rousseau

Senior Scientist, Université Paris-Saclay

Sabrina Amrouche

PhD Student, University of Geneva

Michael Kagan

Lead Staff Scientist, SLAC / Stanford

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, Netta Goussac, Volkan Cevher, Anika Schumann, Nadia Marsan, Massimo Marelli, Anja Kaspersen

09:00-17:00 January 283A

AI & Cities

Konstantin Klemmer, Shin 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