Robert Schapire is a Principal Researcher at Microsoft Research in New York City. He received his PhD from MIT in 1991. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991. In 2002, he became a Professor of Computer Science at Princeton University. He joined Microsoft Research in 2014. His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund). He is a fellow of the AAAI, and a member of both the National Academy of Engineering and the National Academy of Sciences. His main research interest is in theoretical and applied machine learning, with particular focus on boosting, online learning, game theory, and maximum entropy.

Research Interests

Robert Schapire's main research interest is in machine learning, a field that aims to develop automatic methods for making accurate predictions or intelligent decisions based on past experience or observations. Much of his past work has focused on boosting, an approach to machine learning based on the idea of gathering and combining many weak and moderately inaccurate prediction rules. He is also particularly interested in online learning in which the data arrives sequentially, one example at a time. These two problem areas are in fact closely related, and have connections to other research areas, such as game theory and maximum-entropy modeling. Most of his work is concerned with theoretical issues, but he has applied machine learning, for instance, to problems arising in bioinformatics, neuroscience, and the modeling of plant and animal populations.

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Section 34: Computer and Information Sciences