I am a staff research scientist at Meta AI Research (FAIR Team) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, I have worked on a variety of topics, including understanding the lottery ticket hypothesis, self-supervised learning, the mechanisms underlying common regularizers, and the properties predictive of generalization, as well as methods to compare representations across networks, the role of single units in computation, and on strategies to measure abstraction in neural network representations. Previously, I worked at DeepMind in London.
I earned my PhD working with Chris Harvey at Harvard University. For my thesis, I developed methods to understand how neuronal circuits perform the computations necessary for complex behavior. In particular, my research focused on how parietal cortex contributes to evidence accumulation decision-making.
For my undergraduate work, I attended UCSD, where I worked with Fred Gage to investigate the role of REST/NRSF in adult neurogenesis.
When I'm not working, I like to go on adventures with my wife, Julia, and our awesome dogs, Maui and Loki. I'm also a history buff and love learning about the history of science, the two World Wars, and the Cold War.