Ryan Adams is an assistant professor of computer science in the School of Engineering and Applied Sciences. Ryan's research is primarily in the area of machine learning, the subfield of computer science that is concerned with the development of algorithms which can adapt to experience. Ryan focuses on probabilistic approaches to building such algorithms and his work lies at the interface of computer science, statistics and computational neuroscience. In broad terms, he is interested in understanding the computation that lies beneath intelligence and developing artificial systems that can discover complex structure in data. This work enables Ryan to interact with researchers in several different fields and he has won accolades for his work at top international conferences.
Ryan completed his Ph.D. in physics under David MacKay at the University of Cambridge, where he was a Gates Cambridge Scholar and a member of St. John's College. His doctoral work won the honorable mention for the Savage Award for best dissertation in Bayesian theory and methods from the International Society for Bayesian Analysis. Before coming to Harvard, Ryan spent two years as a Junior Research Fellow at the University of Toronto as a part of the Canadian Institute for Advanced Research.