Samuel Barnett is a Ph.D. candidate in Computer Science at Princeton University, advised by Ryan Adams and Tom Griffiths. His research interests include cooperative AI, reinforcement learning, social cognition, and scalable methods for Bayesian machine learning. He was previously the Daniel M. Sachs Scholar at Princeton, and received a Master’s in Mathematics and Philosophy at the University of Oxford, where his dissertation on convergence problems in generative adversarial nets was supervised by Varun Kanade.