I am broadly interested in combining techniques from information theory, dynamical systems, and statistical mechanics to understand how the collective dynamics of biological neural networks give rise to their function. To this end, I am currently working on developing analytical techniques to understand how specific features of functional network structure give rise to low dimensional controllable subspaces of population activity and the important role played by matrix non-normality in this mapping. Other areas of interest include importing techniques from fluid dynamics (Koopman operator approaches) and non-equilibrium stat mech to neural data analysis, as well as probing the representational capacity of artificial recurrent neural networks.
I enrolled in graduate school at Berkeley with the intent of doing experimental condensed matter physics. After about a year of rotating around, it became clear that I was not a “hands on” person, to put it mildly. The Redwood Center seemed happy to accommodate the aimless physicist, and I had done some computational neuroscience research as an undergraduate. It turned out to be a good fit, and I have valued learning from peers whose expertise span many disciplines.