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Eric Mazumdar's research lies at the intersection of machine learning and economics. He is broadly interested in developing tools to understand how to confidently deploy machine learning algorithms in societal-scale systems. His work requires a solid understanding of the theoretical underpinnings of learning algorithms in uncertain, dynamic environments as they interact with strategic agents, including humans and algorithms. Practically, he applies his research in various fields, focusing on learning dynamics, algorithmic game theory, risk and uncertainty, and behavioral economics.
California Institute of Technology • Pasadena, CA
Engaged in teaching and research at the intersection of machine learning and economics.
Most Caltech graduate programs are PhD-only. GRE General and Subject tests vary by department; many have made them optional or no longer accept them.