Chris’s goal is to understand and improve the algorithms that agents can use to learn from data and reason about their experience. This goal is typically framed through the language of statistics, and solved using algorithms for probabilistic inference or optimization. Chris has worked on gradient estimation, variational inference, optimization, and Monte Carlo methods. He is currently interested in learning useful and robust representations, with a particular interest in techniques that learn representations from pretext tasks.  Chris is an Open Philanthropy AI Fellow. Chris received a NIPS Best Paper Award in 2014, and was one of the founding members of the AlphaGo project.