Research
I work on machine learning, which is the study of algorithms that can learn to solve problems from examples.

Large models and transfer learning Large language models like ChatGPT demonstrate that training large models on many inter-related tasks can have a synergistic effect. I am interested in understanding and applying these principles to improve machine learning in data-constrained settings.

Learning and optimization Algorithms for statistical inference and optimization are the engines that drive machine learning. Although inference and optimization may seem like distinct problems, there is a close interplay between them. I am interested in this interplay.

Applications You can improve machine learning algorithms when you know something about the structure of the data. I have long been interested in applications that involve discrete reasoning, for example when we built the first artificial agent that plays the board game Go at a superhuman level. I am now interested in applying these principles to biochemistry.