I'm a postdoc in the Department of Applied Physics at Yale. My work focuses on applying information theory and statistical mechanics to generative AI algorithms, in particular, diffusion models.
I am broadly interested in importing ideas from physics to machine learning. I love cross-pollinating ideas from different disciplines. This approach stems from my core belief that the universe does not self-factorize into distinct academic disciplines. Nature is economical in its creativity; the same structural motifs often reappear in problems that, at first glance, seem unrelated.
I did my PhD in Theoretical Physics from the University of California San Diego under the nurturing guidance of Daniel Green. After that, I spent three eventful years at the University of Chicago under Austin Joyce. It was at Chicago that I became interested in diffusion models. I also benefitted from the mentorship of Lorenzo Orecchia at this time.
