Biography:Geoffrey J. Gordon

From HandWiki
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
  • Joelle Pineau
Websitehttps://www.cs.cmu.edu/~ggordon/

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.

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)". https://www.cs.cmu.edu/~ggordon/chatter.ps.gz. 
  2. "Award Offers and Honorable Mentions List". https://www.research.gov/grfp/AwardeeList.do?method=loadAwardeeList. 
  3. "Geoff's Home Page". https://www.cs.cmu.edu/~ggordon/. 
  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. 9.0 9.1 "Geoff Gordon" (in en-US). https://www.microsoft.com/en-us/research/people/ggordon/. 
  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, doi:10.1145/1401890.1401969, ISBN 978-1-60558-193-4 
  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.