Geoffrey J. Gordon
From Wikipedia, the free encyclopedia
Geoffrey J. Gordon is a professor at the Machine Learning Department at Carnegie Mellon University in Pittsburgh[3] and director of research at the Microsoft Montréal lab.[4][5][6][7][8][9] He is known for his research in statistical relational learning[10] (a subdiscipline of artificial intelligence and machine learning) and on anytime dynamic variants of the A* search algorithm.[11] His research interests include multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data (e.g. maps, video, text), computational learning theory, and game theory.
- Cornell University (BA)
Geoff Gordon | |
|---|---|
| Awards | |
| Academic background | |
| Education |
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| Alma mater | Carnegie Mellon University (PhD) |
| Thesis | Approximate Solutions to Markov Decision Processes (1999) |
| Doctoral advisor | Tom M. Mitchell |
| Academic work | |
| Institutions | Carnegie Mellon University |
| Doctoral students | |
| Website | https://www.cs.cmu.edu/~ggordon/ |
Gordon received a B.A. in computer science from Cornell University in 1991, and a PhD at Carnegie Mellon in 1999.[9]
