applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
Avi Feller is an assistant professor in the Goldman School of Public Policy and the Department of Statistics at UC Berkeley. His methodological research centers on learning more from social policy evaluations, especially randomized experiments. His applied research focuses on working with governments on using data to design, implement, and evaluate policies. Prior to his doctoral studies, Feller served as Special Assistant to the Director at the White House Office of Management and Budget and worked at the Center on Budget and Policy Priorities. Feller received a Ph.D. in Statistics from Harvard University, an M.Sc. in Applied Statistics as a Rhodes Scholar at the University of Oxford, and a B.A. in Political Science and Applied Mathematics from Yale University.