Mansheej Paul is a Graduate Student Fellow at the Reglab and the Neural Dynamics and Computation Lab. He holds a B.Sc. in Applied Mathematics from Brown University and is currently a Ph.D. candidate in the Department of Applied Physics at Stanford University.  Before joining the Reglab, he has worked on random matrix theory in mathematical physics, and reinforcement learning models in computational neuroscience. At the Reglab, his research focuses on using AI to optimally allocate scarce government resources. He is also interested in understanding the optimization landscape of deep neural networks.