- Variational inference
- Monte Carlo methods
- First-order optimization
Chris works on methods for machine learning, with an emphasis on those that work at scale in deep learning applications. Chris is particularly interested in methods for numerical integration and optimization. So far Chris has worked on gradient estimation, variational inference, Monte Carlo methods, and first-order methods for optimization.
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.
- Open Philanthropy AI Fellow
- NIPS Best Paper Award in 2014
- Founding members of the AlphaGo project