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Amy Greenwald
American computer scientist From Wikipedia, the free encyclopedia
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Amy Rachel Greenwald is an American computer scientist focusing on machine learning in algorithmic game theory, including the study of intelligent agents, multi-agent systems, and reinforcement learning for Nash equilibria, artificial intelligence in negotiation, and computer play of poker.[1] She is a professor of computer science at Brown University.[2]
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Education and career
Greenwald was a double major in computer science and economics at the University of Pennsylvania, graduating summa cum laude in 1991. She went to the University of Oxford as a Thouron Scholar, and received a master's degree in computation there in 1992. Returning to the US, she received a second master's degree in computer science in 1995 at Cornell University before completing her Ph.D. in computer science at New York University in 1999.[3] Her doctoral dissertation, Learning to Play Network Games, was jointly supervised by Bud Mishra and Rohit Parikh.[4]
After a year of postdoctoral research at the IBM Thomas J. Watson Research Center, Greenwald became an assistant professor at Brown University in 2000. She was promoted to associate professor in 2008 and full professor in 2018.[3]
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Book
Greenwald is a coauthor of the book Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (with Michael P. Wellman and Peter Stone, MIT Press, 2007).
Recognition
Greenwald received the National Science Foundation CAREER Award in 2002.[3] In the same year, she received the Presidential Early Career Award for Scientists and Engineers, "for advancing a theory about how automated software agents can make decisions in uncertain environments such as online auctions".[5] She received the Alfred P. Sloan Research Fellowship in 2006.[3]
Several students coached by Greenwald have medaled in the Supply Chain Management League of the International Automated Negotiation Agents Competition, in 2019, 2021, and 2024.[6]
References
External links
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