His research lies at the interface between theoretical machine learning and economics, primarily focusing on domains such as information elicitation and crowdsourcing which involve the exchange of information for money, and drawing techniques from convex analysis, game theory, optimization, and statistics. Before coming to Boulder, Rafael was a postdoc at the Center for Research on Computation and Society at Harvard University and at Microsoft Research New York, and in 2013 received his Ph.D. in Computer Science at UC Berkeley, advised by Christos Papadimitriou and supported by the NDSEG Fellowship.