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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. This research necessitates a deep understanding of the theoretical underpinnings of learning algorithms, particularly in uncertain and dynamic environments where strategic agents—humans and algorithms—interact. Practically, his work applies concepts from Algorithmic Game Theory, Social Learning Dynamics, and Behavioral Economics to address these challenges. Mazumdar's academic background includes a B.S. from the Massachusetts Institute of Technology in 2015 and a Ph.D. from the University of California, Berkeley in 2021. His affiliation with Caltech has allowed him to contribute significantly to the Center for Social Information Sciences.
California Institute of Technology • Pasadena, CA
Teaching and conducting research at the intersection of machine learning and economics.
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