Hello!

I am a theoretical neuroscientist and research fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University.

A general theory of neural circuits must link synaptic connectivity, large-scale neuronal activity, and the tasks these circuits perform. I build such theories using statistical physics—which naturally accommodates the ubiquitous disorder in large neural circuits—as well as tools from machine learning. The unifying goal is to connect high-dimensional nonlinear network models to the complex, heterogeneous data that modern experiments produce.

I earned my Ph.D. in Neurobiology and Behavior from Columbia University in the Center for Theoretical Neuroscience, where I was primarily advised by Larry Abbott and worked closely with Ashok Litwin-Kumar and Haim Sompolinsky. Before that, I studied physics and computer science at UC Berkeley.

My publications are listed below, or see Google Scholar. My CV is here.

Outside of science, I see a lot of Broadway.

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