Geoffrey J. Gordon

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
Alma materCarnegie Mellon University (PhD)
ThesisApproximate Solutions to Markov Decision Processes (1999)
Doctoral advisorTom M. Mitchell
Academic work
InstitutionsCarnegie Mellon University
Doctoral students
Websitehttps://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
  1. Gordon, Geoffrey J. (4 April 1996). "Chattering in SARSA(λ)". Retrieved 17 August 2022.
  2. "Award Offers and Honorable Mentions List". Graduate Research Fellowship Program (GRFP). Retrieved 17 August 2022.
  3. "Geoff's Home Page". www.cs.cmu.edu. Retrieved 2018-08-04.
  4. Microsoft appoints Carnegie Mellon professor to head expanded Montreal AI research lab, itbusiness.ca, 2018-01-24
  5. 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.
  6. Here's Why Canada Can Win The AI Race, Forbes, 2018-03-13
  7. Canadian Tech Sector Thrives, but Struggles to Keep Its Talent, Wall Street Journal, 2018-02-08.
  8. Microsoft announces expansion of Montreal AI research lab, windowscentral, 2018-01-24.
  9. "Geoff Gordon". Microsoft Research. Retrieved 2018-08-04.
  10. 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
  11. 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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