Physicist &
ML Researcher
PhD candidate studying generative models for physical systems and building foundation models for science.
University of Toronto, Vector Institute, Berkeley Lab (NERSC)
PhD advised by Yoni Kahn, committee: David Curtin & Chris Maddison
NERSC advised by Wahid Bhimji, Benjamin Nachman, Aishik Ghosh
Physics for AI, AI for Physics.
Select Papers
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arXiv preprint, 2026
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arXiv preprint, 2026
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The Omni family: cross-domain scientific foundation modelsTransferring particle-physics knowledge to molecular dynamics and cosmology.
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arXiv preprint, 2025
Research Focus
Physics Data for Understanding ML
Leveraging the controllable generation and known symmetries of physics datasets to probe the internal mechanisms of deep neural networks.
Physics-Inspired Theory for Scaling Laws
Using effective field theory to predict neural-network ensemble behavior and derive uncertainty scaling laws without training an ensemble.
Automating Scientific Model Building
Developing ML for “theory inversion”: parameter estimation with uncertainty quantification, simulation emulation, and automated theory writing.
Cross-Domain Foundation Models
Foundation models for scientific point clouds that transfer knowledge across particle physics, cosmology, and molecular dynamics.
