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I am a postdoctoral fellow at the Center for Computational Mathematics of the Flatiron Institute. I am broadly interested in the theory of machine learning. Active projects include using ideas from numerical integration to develop computationally efficient adaptive step size schemes for optimization, and studying the statistical efficiency of score estimation in the context of score-based diffusion modeling.
I graduated with a PhD from the University of California, Berkeley in May of 2022. My advisors were Michael I. Jordan and Michael R. DeWeese . I was also affiliated with the Statistical AI Learning group, the Berkeley AI Research group, and the Redwood Center for Theoretical Neuroscience .
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