Workshop II: Interpretable Learning in Physical Sciences

October 14 - 18, 2019

Schedule


Monday, October 14, 2019

9:00 - 9:50
Cecilia Clementi (Rice University)

Learning molecular model from simulation and experimental data
PDF Presentation

 
10:15 - 11:05
11:30 - 12:20
2:30 - 3:20
Shirley Ho (Flatiron Institute/Princeton University)

Simulating the Universe with Deep Learning

 
3:45 - 4:35

Tuesday, October 15, 2019

9:00 - 9:50
Weinan E (Princeton University)

Machine learning based multi-scale modeling
PDF Presentation

 
10:15 - 11:05
11:30 - 12:20
2:30 - 3:20
Atilim Gunes Baydin (University of Oxford)

Universal Probabilistic Programming in Simulators
PDF Presentation

 
4:00 - 4:50

Wednesday, October 16, 2019

9:00 - 9:50
 
10:15 - 11:05
11:30 - 12:20
Peter Battaglia (DeepMind Technologies)

Learning structured models of physics
PDF Presentation

 
2:00 - 2:50
Judea Pearl (University of California, Los Angeles (UCLA))

Interpretability and explainability from a causal lens
PDF Presentation

 
3:15 - 4:05
Adji Bousso Dieng (Columbia University)

Structured Deep Generative Models
PDF Presentation

 
4:30 - 5:20
Gianni De Fabritiis (Universitat Pompeu Fabra)

Can we machine learn drug discovery?


Thursday, October 17, 2019

9:00 - 9:50
10:15 - 11:05
Anatole von Lilienfeld (University of Basel)

Quantum Machine Learning

 
11:30 - 12:20
Kieron Burke (University of California, Irvine (UCI))

Density functionals from machine learning
PDF Presentation

 
2:30 - 3:20
4:00 - 4:50
Zachary Lipton (Carnegie Mellon University)

Interpretability: of what, for whom, why, and how?

 

Friday, October 18, 2019

9:00 - 9:50
10:15 - 11:05
Pratyush Tiwary (University of Maryland)

Learning to learn, learning to forget

 
11:30 - 12:20
2:30 - 3:20
Francesco Paesani (University of California, San Diego (UCSD))

Data-driven models for predictive molecular simulations
PDF Presentation

 
4:00 - 4:50