Rafael Frongillo

Rafael Frongillo is an Associate Professor of Computer Science at the University of Colorado Boulder and holds the Roubos Engineering Endowed Faculty Fellowship. His research bridges theoretical machine learning and economics, focusing on information elicitation, crowdsourcing, and mechanisms involving monetary incentives. He employs techniques from convex analysis, game theory, and statistics to address challenges in prediction markets, peer prediction, and algorithmic decision-making. Frongillo earned his Ph.D. in Computer Science from UC Berkeley in 2013, advised by Christos Papadimitriou, and held postdoctoral positions at Harvard University’s Center for Research on Computation and Society and Microsoft Research New York.

Education:

• Ph.D. in Computer Science, UC Berkeley (2013), advised by Christos Papadimitriou
• Postdoctoral Researcher, Harvard University (2013–?), Microsoft Research New York (2013–?)

Research Interests: Frongillo’s work emphasizes the intersection of machine learning and economics, particularly in designing systems that incentivize truthful information sharing. Key themes include:

• Information elicitation via scoring rules and prediction markets
• Algorithmic game theory for crowdsourcing and peer prediction
• Optimization techniques for loss functions and statistical consistency
• Computational aspects of economic mechanisms

Awards & Fellowships:

• Roubos Engineering Endowed Faculty Fellow
• NDSEG Fellowship (2013)

Grants & Advising: While specific grants are not detailed, his research has been supported by fellowships and institutional endowments. No formal advisee names are listed in the provided texts.

Labs & Collaborations: Active in interdisciplinary collaborations at CU Boulder and prior institutions, though specific lab affiliations are not mentioned.