Time Monday Oct 16Tuesday Oct 17Wednesday Oct 18
8:30-9:00 Breakfast/WelcomeBreakfastBreakfast
9:00-9:45P. Alves (UCLA)
Distilling reduced plasma physics models from the data of first-principles kinetic simulations
G. Karniadakis (Brown)
Neural PDEs and Neural Operators for Physics-Informed Machine Learning
M. Stoudenmire (Flatiron)
Quantum-Inspired Tensor Network Methods for Machine Learning
9:45-10:30M. McCabe (Flatiron)
Challenges and Opportunities in Learning Dynamics
R. Walters (Northeastern)
Equivariant Neural Networks for Learning Dynamics
Y. W. Li (LANL)
Hamiltonian extraction and inverse modeling of materials
Coffee BreakCoffee BreakCoffee Break
11:00-11:45C. Hall (UGa)
Finding hidden forming exoplanets using machine learning
V. Vitelli (UChicago)
Machine learning interpretable models of cell biophysics from data
G. Carleo (EPFL)
Describing Interacting Many-Body Systems with Neural-Network Quantum States
11:45-12:30M. Golden (GT)
Data Driven Model Discovery for Dynamical Systems
C. Rozell (GT)
Distorted dynamics: Using explainable AI to track depression recovery with deep brain stimulation
E. Kim (Cornell)
Data-centric learning of Quantum Many-body States
Lunch BreakLunch BreakEnd of Conference
2:00-2:45A. Liu (UPenn)
Physical systems that learn on their own
C. Zhang (GT)
Large pretrained models for material science
2:45-3:30N. Loureiro (MIT)
AI/ML as tools of scientific discovery in plasma physics: requirements and opportunities
G. Iannacchione (NSF/DMR)
S. Vahidinia (NASA)
A. Berlind (NSF/AST)

AI/ML Funding Landscape
Coffee BreakCoffee Break
4:00-5:00M. Kunda (Vanderbilt)
What do people see in scatterplots? Research at the intersection of AI, autism, and visual thinking
Panel Discussion with
Agency POs