Rafael M. Frongillo
Raf is a computer scientist working in theoretical machine learning and at the interface with economics, primarily focusing on problems such as crowdsourcing or prediction markets which involve the exchange of information for money. His research draws techniques from convex analysis, game theory, optimization, and theoretical statistics. He also continues to enjoy a thread of research in dynamical systems, applying computational topological tools to develop automated proof systems for the rigorous analysis of dynamical systems. Before coming to Harvard, Raf was a postdoc at Microsoft Research in New York City, and he earned his Ph.D. at UC Berkeley, advised by Christos Papadimitriou and supported by the NDSEG Fellowship.