Physicist &
ML Researcher
PhD candidate at the University of Toronto. Building foundation models for science at Berkeley Lab/NERSC.
Physics for AI,
AI for Physics.
My research has been/is focused on using physics-inspired theory and physics data to understand neural network behavior, building foundation models that transfer across scientific domains, and developing AI tools to automate scientific discovery.
I am a PhD candidate at the University of Toronto advised by Prof. Yoni Kahn and doctoral researcher at Berkeley Lab (NERSC) advised by Wahid Bhimji and Benjamin Nachman.
Research Interests
We hope to use physics to build better AI, and use AI to do physics better.
Physics Data for Understanding ML
We hope to leverage the unique properties of physics datasets (access to controllable data generation and known data symmetry and structure) to probe the internal mechanisms of deep neural networks.
Physics-Inspired Theory for Scaling Laws
Using an effective field theory to predict how neural network ensembles behave, deriving scaling laws for uncertainty quantification without the need to train an ensemble.
Automating Scientific Model Building
Developing ML methods with the goal of “theory inversion”. Parameter estimation with Uncertainty Quantification, Simulation Emulation, and Automating Theory Writing.
Cross-Domain Foundation Models
Building foundation models for scientific point clouds (irregular graphs!) that transfer knowledge across the domains of particle physics, cosmology, and molecular dynamics.
