I am generally interested in the theoretical foundations of machine learning, in statistical mechanics, and in the interface of computer science, high-dimensional statistics, and statistical mechanics. All my projects eventually distill down to the question of how to solve a specific optimization problem. Currently active projects include leveraging ideas from numerical integration to construct adaptive step size routines in gradient-based optimization, and developing perturbative solutions to the Fokker-Planck equation for a driven Brownian system.
I am a graduate student in the Biophysics Graduate Group. Before I came to Berkeley, I was a Junior Research Fellow at the National Center for Biological Sciences in Bangalore, India. Before that, I completed a Masters degree in theoretical physics at the Perimeter Institute for Theoretical Physics in Waterloo, Canada. I was an undergraduate at Amherst College, where I received a degree in physics.
When I’m not working I enjoy cooking, reading (especially recipe books), playing piano, and taking long walks.
You can find my personal webpage here.