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.
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]
References
edit- ^ Gordon, Geoffrey J. (4 April 1996). "Chattering in SARSA(λ)". Retrieved 17 August 2022.
- ^ "Award Offers and Honorable Mentions List". Graduate Research Fellowship Program (GRFP). Retrieved 17 August 2022.
- ^ "Geoff's Home Page". www.cs.cmu.edu. Retrieved 2018-08-04.
- ^ Microsoft appoints Carnegie Mellon professor to head expanded Montreal AI research lab, itbusiness.ca, 2018-01-24
- ^ Leaders in Davos acknowledge AI’s potential for good, but point to unanswered questions, Justin Trudeau twittering about Gordons appointment from WEF, itbusiness.ca. 2018-01-24.
- ^ Here's Why Canada Can Win The AI Race, Forbes, 2018-03-13
- ^ Canadian Tech Sector Thrives, but Struggles to Keep Its Talent, Wall Street Journal, 2018-02-08.
- ^ Microsoft announces expansion of Montreal AI research lab, windowscentral, 2018-01-24.
- ^ a b "Geoff Gordon". Microsoft Research. Retrieved 2018-08-04.
- ^ Singh, Ajit P.; Gordon, Geoffrey J. (1) (2008), "Relational Learning via Collective Matrix Factorization", Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '08, New York, NY, USA: ACM, pp. 650–658, CiteSeerX 10.1.1.141.6607, doi:10.1145/1401890.1401969, ISBN 978-1-60558-193-4, S2CID 9683534
{{citation}}: CS1 maint: numeric names: authors list (link) - ^ Likhachev, Maxim; Gordon, Geoff; Thrun, Sebastian. "ARA*: Anytime A* search with provable bounds on sub-optimality". In S. Thrun, L. Saul, and B. Schölkopf, editors, Proceedings of Conference on Neural Information Processing Systems (NIPS), Cambridge, MA, 2003. MIT Press.
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