5-8 p.m. — Early Registration
6-8 p.m. — Welcome Reception, Light Reception Fare will be served
8-9 a.m. — Breakfast
9-9:30 a.m. — Introduction, Alan Bishop, Principle Associate Director of Science, Technology, and Engineering at Los Alamos National Laboratory
9:30-10:10 a.m. — Invited Talk, Carlo Baldassi, "Synaptic [classical and quantum] Fluctuations as a Recipe for Robust and Efficient Neural Network Training" Presentation
10:10-10:50 a.m. — Invited Talk, Greg Ver Steeg, "Blessings of Dimensionality for Gaussian Latent Factor Models" Presentation
10:50-11:20 a.m. — Break
11:20-noon — Invited Talk, Alex Gorodetsky, "Multifidelity Model Management using Latent Variable Bayesian Networks"
Noon-12:20 p.m. — Contributed Talk, Insu Han, "Approximating and Optimizing Spectral Matrix Functions for Large-scale Matrices using Stochastic Chebyshev Expansion" Presentation
12:20-2 p.m. — Lunch
2-2:40 p.m. — Invited Talk, David Saad, "Error and Entropy in the Function Space of Multi-layer Networks" Presentation
2:40-3:20 p.m. — Invited Talk, Surya Ganguli, "On the Beneficial Role of Dynamic Criticality and Chaos in Deep Learning"
3:20-3:50 p.m. — Break
3:50-4:30 p.m. — Invited Talk, Paul Johnson, "Machine Learning in Faulting Physics"
4:30-5:10 p.m. — Invited Talk, Eric Mjolsness, "Multiscaling with Learning in Stochastic Physical Models"
9-9:40 a.m. — Invited Talk, Miles Stoudenmire, "Learning Relevant Features of Data with Multiscale Tensor Networks" Presentation
9:40-10:20 a.m. — Invited Talk, Dong-Lin Deng, "Machine Learning Quantum States, Many-body Entanglement and Bell Non-locality"
10:20-10:50 a.m. — Break
10:50-11:30 a.m. — Invited Talk, Marc Vuffray, "Unraveling Quantum Annealers with Machine Learning" Presentation
11:30-11:50 a.m. — Contributed Talk, Marin Bukov, "Reinforcement Learning in Different Phases of Quantum Control" Presentation
11:50-12:10 p.m. — Contributed Talk, Sungsoo Ahn, "Renormalization Group of Graphical Models"
12:10-2 p.m. — Lunch
2-2:40 p.m. — Invited Talk, Yan Liu, "Neural Interaction Detector - Detecting High-order Interactions via Deep Neural Networks" Presentation
2:40-3:20 p.m. — Invited Talk, Jinwoo Shin, "Confident Neural Networks"
3:50-4:30 p.m. — Invited Talk, Diane Oyen, "Discovering Meaningful Relationships in Data: Privileged Information in Graphical Model Structure Learning"
4:30-4:50 p.m. — Contributed Talk, Anuj Karpatne, "How can Physics Inform Deep Learning Methods in Scientific Problems?: Recent Progress and Future Prospects"
9-9:40 a.m. — Invited Talk, Gregory Valiant, "When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution"
9:40-10:20 a.m. — Invited Talk, Pradeep Ravikumar, "The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities" Presentation
10:50-11:30 a.m. — Invited Talk, Sidhant Misra, "Sample-optimal Learning of Sparse Graphical Models"
11:30-11:50 a.m. — Contributed Talk, Yeesian Ng, "Statistical Learning for DC Optimal Power Flow"
11:50-12:20 p.m. — Contributed Talk, Chiyu Jiang, " Deep Learning Based Geometry Encoding for Physics Aware Surrogate Modeling and Transfer Learning" Presentation
2-2:40 p.m. — Invited Talk, Karthik Duraisamy, "Data-enabled, Physics-constrained Predictive Modeling of Complex Systems"
2:40-3:20 p.m. — Invited Talk, Gowri Sirinivasan, "Discovering Reduced Graph-based Models of Fracture Networks using Machine Learning"
3:50-4:50 p.m. — Discussion Session- "What does Physics Informed Machine Learning mean to you?"
5:00-6:30 p.m. — Poster Session
6:30-8:30 p.m. — Banquet Dinner
9:40-10:00 a.m. — Contributed Talk, Justin Smith, "Chemical Space Sampling with Active Learning"
10:00-10:20 a.m. — Contributed Talk, Konstantin Gubaev, "Machine Learning Interatomic Potentials forMulticomponent Systems" Presentation
10:50-11:30 a.m. — Invited Talk, Luca Biferale, "Flow Navigation by Smart Particles via Reinforcement Learning" Presentation
11:30-11:50 a.m. — Contributed Talk, Arvind Mohan, "Reduced Order Modeling of Turbulent Flows using Long Short Term Memory Neural Networks" Presentation
11:50-12:10 p.m. — Contributed Talk, Oliver Hennign, "Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks"
2-2:40 p.m. — Invited Talk, Ryan King, "Deep Learning for Generating Fully-Developed Turbulent Flows"
2:40-3:20 p.m. — Invited Talk, Heng Xiao, "Physics-Informed Machine Learning for Predictive Turbulence Modeling"
3:50-4:30 p.m. — Invited Talk, Garrett Kenyon, "Modeling How Brains Learn about Physics"
4:30-4:50 p.m. — Contributed Talk, Alireza Alemi, "Exponential Capacity in an Autoencoder Neural Network" Presentation