About Me

I’m a PhD student in the Applied Mathematics department at the University of Washington in Seattle. I’m working with Eric Shea-Brown from UW and Stefan Mihalas from Allen Institute of Brain Science on flexible circuit architectures in vision – both in Theoretical Neuroscience to understand optimal visual processing in different contexts in the brain, and in Machine Learning to design multi-functional deep networks.

Before this, I was an undergraduate in the Math department at Princeton University and subsequently a Research Assistant in the Mechanical Engineering department there.

I am generally interested in Math models of all sorts that could shed light on how the world works and make more accurate predictions. However, my true passion is in the field of Computational Neuroscience, where I use computational tools and design theoretical models to figure out the design principles of the brain and to build better artificial neural architectures that would be more flexible and general-purpose.