Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics

September 23 - 27, 2019

Schedule


Monday, September 23, 2019

9:00 - 9:50
Alexandre Tkatchenko (University of Luxembourg)

Towards a Unified Machine Learning Model of Molecular Chemical Space
PDF Presentation

 
10:15 - 11:05
Xavier Bresson (Nanyang Technological University, Singapore)

Graph Convolutional Neural Networks for Molecule Generation
PDF Presentation

 
11:30 - 12:20
Joshua Bloom (University of California, Berkeley (UC Berkeley))

Physics-Informed (and -informative) Generative Modelling in Astronomy

 
2:30 - 3:20
Laurent Dinh (Google)

A primer on normalizing flows
PDF Presentation

 
3:45 - 4:35
Frank Noe (Freie Universität Berlin)

Deep Generative Learning for Physics Many-Body Systems
PDF Presentation

 

Tuesday, September 24, 2019

9:00 - 9:50
Philip Kurian (Howard University)

New horizons in quantum biology: Learning complexity, emergence, and coherence in living matter

 
10:15 - 11:05
Olexandr Isayev (Carnegie Mellon University)

De Novo Drug Design with Deep Reinforcement Learning

11:30 - 12:20
Nicola De Cao (University of Amsterdam)

Deep Generative Models for Molecular Graphs
PDF Presentation

 
2:30 - 3:20
4:00 - 4:50
Rafael Gomez-Bombarelli (Massachusetts Institute of Technology)

Coarse graining autoencoders and evolutionary learning of atomistic potentials
PDF Presentation

 

Wednesday, September 25, 2019

9:00 - 9:50
Alán Aspuru-Guzik (University of Toronto)

Generative models for molecules.

10:15 - 11:05
Ross King (University of Science and Technology in Manchester (UMIST))

Automating Science using Robot Scientists
PDF Presentation

 
11:30 - 12:20
2:30 - 3:20
4:00 - 4:50

Thursday, September 26, 2019

9:00 - 9:50
Phiala Shanahan (Massachusetts Institute of Technology)

Generative models for lattice quantum field theory
PDF Presentation

 
10:15 - 11:05
11:30 - 12:20
2:30 - 3:20
4:00 - 4:15
Joshua Yao-Yu Lin (University of Illinois at Urbana-Champaign)

Hunting for dark matter substructures in strong lensing with neural networks.
PDF Presentation

4:20 - 4:35
Nicholas Charron (Rice University)

Coarse-graining molecular systems by spectral matching.

4:40 - 4:55
Ganesh Sivaraman (Argonne National Laboratory)

A diversified machine learning strategy for predicting and understanding molecular melting points.
PDF Presentation


Friday, September 27, 2019

9:00 - 9:50
Benjamin Nachman (Lawrence Berkeley National Laboratory)

Likelihood free generative modeling for high energy physics
PDF Presentation

 
10:15 - 11:05
11:30 - 12:20
Juan Felipe Carrasquilla Alvarez (Vector Institute)

Simulating quantum circuits with neural machine translation
PDF Presentation

 
2:30 - 3:20
Giuseppe Carleo (Flatiron Institute, a Division of the Simons Foundation)

Generative and variational modeling for quantum many-body physics

 
4:00 - 4:50