I am a postdoctoral researcher at the Simons Institute at UC Berkeley, where I am part of the the Foundations of Data Science Institute (FODSI). I am broadly interested in theoretical machine learning and data-driven control, especially 1) online learning, deep learning, and statistical learning theory, and 2) learning-based approaches to control, with potential applications including robotics and cyberphysical systems.
I am currently studying deep autoregressive architectures such as Transfomers, which underpin Large Language Models (LLMs). I am exploring several theoretical questions associated with these models, including how gradient descent behaves when training these models, how model performance scales with training data (neural scaling laws), and how these models can be used for system identification, Kalman filtering, and control.
Before moving to Berkeley I was a PhD student in the Computing and Mathematical Sciences (CMS) department at Caltech, where I was extremely fortunate to be advised by Babak Hassibi. My thesis was awarded the Bhansali Family Doctoral Prize in Computer Science, which is awarded by the CMS department to a single outstanding dissertation in computer science each year.
For more information, please see my CV.
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