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Geoffrey J. Gordon

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Geoffrey J. Gordon is a professor at the Machine Learning Department at Carnegie Mellon University in Pittsburgh[1] and director of research at the Microsoft Montréal lab.[2][3][4][5][6][7] He is known for his research in statistical relational learning[8] (a subdiscipline of artificial intelligence and machine learning) and on anytime dynamic variants of the A* search algorithm.[9] 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.

Gordon received a B.A. in computer science from Cornell University in 1991, and a Phd at Carnegie Mellon in 1999.[7]

References

  1. ^ "Geoff's Home Page". www.cs.cmu.edu. Retrieved 2018-08-04.
  2. ^ Microsoft appoints Carnegie Mellon professor to head expanded Montreal AI research lab, itbusiness.ca, 2018-01-24
  3. ^ 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.
  4. ^ Here's Why Canada Can Win The AI Race, Forbes, 2018-03-13
  5. ^ Canadian Tech Sector Thrives, but Struggles to Keep Its Talent, Wall Street Journal, 2018-02-08.
  6. ^ Microsoft announces expansion of Montreal AI research lab, windowscentral, 2018-01-24.
  7. ^ a b "Geoff Gordon". Microsoft Research. Retrieved 2018-08-04.
  8. ^ 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{{citation}}: CS1 maint: numeric names: authors list (link)
  9. ^ 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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