Google Research

Daniel S. Park


I researched problems in string theory and quantum field theory in academia for most of my professional career. After receiving my PhD in physics from MIT, I continued my research on topics at the boundary of mathematics and high energy theoretical physics as a postdoc at the Simons Center for Geometry and Physics at Stony Brook University and the New High Energy Theory Center at Rutgers University. Post academia, I went on to work at a pharmaceutical company, improving their computational platform for drug discovery. I joined Google as an AI resident after being there for a year. As a resident, I have been mostly working on understanding the theoretical aspects of learning, and studying measures that can be used to characterize the macroscopic behavior of neural networks. The ultimate fantasy would be to derive relations among macroscopic quantities that can be used to predict, for example, the performance of networks. I am also interested in how to make machines learn robust and efficient representations. The AI residency has provided me the opportunity to witness the frontier of the ever-burgeoning field of machine learning and to work with the very people that are pushing at the boundaries. It has been an exciting journey so far.

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