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I am a physicist seeking ways to understand generalization in deep learning. Towards this goal, I study AI as a scientific discipline, where both empirical validity and explainable methodologies are essential. For instance, see [here] for our work on generalization and optimization in physics-informed neural networks, and see [here] for our work on double descent and online/offline correspondence in similarity learning. 

 

Previously, I explored the construction of models to explain small- and large-scale phenomena in the universe. I worked on the interplay between particle physics and cosmology, learning from the early universe how to build and interpret our models of particle physics. See [here] for all publications.