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Yasaman Bahri
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At present, my research program is to work towards a scientific understanding of deep learning. Typically this has involved some theoretical analysis as well empirical work to tease out phenomena. A primary goal is to try to elucidate the underpinnings of current successes and shortcomings in deep learning (beginning with supervised learning) and leverage these towards more general machine intelligence. In particular, this will involve understanding the interplay between (i) algorithms (the traditional domain of computer science) with (ii) models and (iii) tasks.
I was trained as a theoretical quantum condensed matter physicist and received my Ph.D. in Physics from UC Berkeley in 2017. I am also interested in connections between theoretical physics and machine learning where these connections would be well-motivated by the problem at hand.
My prior expertise is specifically in the field of quantum many-body theory. I was fortunate to have Professor Ashvin Vishwanath as my thesis advisor. My interests have always been broad, and I worked on several disparate areas as part of my doctoral research, including topological phases, many-body localization, non-Fermi liquids, and topological mechanical systems. I got started in theory as an undergraduate through research on tensor networks and entanglement in quantum systems with Professor Joel Moore at Berkeley.