Time	Monday Oct 16	Tuesday Oct 17	Wednesday Oct 18
8:30-9:00	Breakfast/Welcome	Breakfast	Breakfast
9:00-9:45	P. 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
9:45-10:30	M. 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 Break	Coffee Break	Coffee Break
11:00-11:45	C. 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
11:45-12:30	M. 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 Break	Lunch Break	End of Conference
2:00-2:45	A. Liu (UPenn) Physical systems that learn on their own	C. Zhang (GT) Large pretrained models for material science
2:45-3:30	N. 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 Break	Coffee Break
4:00-5:00	M. Kunda (Vanderbilt) What do people see in scatterplots? Research at the intersection of AI, autism, and visual thinking	Panel Discussion with Agency POs
6:00-7:00		Reception