Eric Horvitz

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Eric Joel Horvitz (born April 14, 1958) is an American computer scientist, and Distinguished Scientist at Microsoft, where he serves as co-director of Microsoft Research's main Redmond lab.[1]


Horvitz received his PhD in 1990 and his MD degree at Stanford University.[2] His doctoral dissertation was titled Computation and action under bounded resources.

He is currently Distinguished Scientist at Microsoft, where he serves as co-director of Microsoft Research's main Redmond lab.

He has been elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and of the American Association for the Advancement of Science (AAAS), and was elected to the ACM CHI Academy in 2013.

He currently serves on the NSF Computer & Information Science & Engineering (CISE) Advisory Board and on the council of the Computing Community Consortium (CCC).


Horvitz's research interests span theoretical and practical challenges with developing systems that perceive, learn, and reason. His contributions include advances in principles and applications of machine learning and inference, information retrieval, human-computer interaction, bioinformatics, and e-commerce.

Horvitz played a significant role in establishing the credibility of artificial intelligence with other areas of computer science and computer engineering, influencing fields ranging from human-computer interaction to operating systems. His research helped establish the link between artificial intelligence and decision science. As an example, he coined the concept of bounded optimality, a decision-theoretic approach to bounded rationality.[3]

Horvitz speaks on the topic of artificial intelligence around the world, including on NPR and the Charlie Rose show.[4][5][6] His research has been featured in the New York Times and the Technology Review.[7][8][9][10]



  • 1990. Computation and action under bounded resources.
  • 1990. Toward normative expert systems: The Pathfinder project. With David Earl Heckerman, and Bharat N. Nathwani. Knowledge Systems Laboratory, Stanford University, 1990.

Articles, a selection:

  • Horvitz, Eric J., John S. Breese, and Max Henrion. "Decision theory in expert systems and artificial intelligence." International Journal of Approximate Reasoning 2.3 (1988): 247-302.
  • Henrion, Max, John S. Breese, and Eric J. Horvitz. "Decision analysis and expert systems." AI magazine 12.4 (1991): 64.
  • Shwe, Michael A., et al. "Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base." Methods of information in Medicine 30.4 (1991): 241-255.
  • Horvitz, Eric J., Martin L. Sonntag, and Michael E. Markley. "Display system and method for displaying windows of an operating system to provide a three-dimensional workspace for a computer system." U.S. Patent No. 5,880,733. 9 Mar. 1999.
  • Horvitz, Eric J. "Reasoning about beliefs and actions under computational resource constraints." arXiv preprint arXiv:1304.2759 (2013).


  1. ^ "Eric Horvitz: Distinguished Scientist". 
  2. ^ t/275 "Big Thinkers Event: Eric Horvitz, Machine Intelligence and the Open World". Retrieved 12 March 2011. 
  3. ^ Mackworth, Alan (July 2008). "Introduction of Eric Horvitz". AAAI Presidential Address. 
  4. ^ Hansen, Liane (21 March 2009). "Meet Laura, Your Virtual Personal Assistant". NPR. Retrieved 16 March 2011. 
  5. ^ Kaste, Martin (11 Jan 2011). "The Singularity: Humanity's Last Invention?". NPR. Retrieved 14 Feb 2011. 
  6. ^ Rose, Charlie. "A panel discussion about Artificial Intelligence". 
  7. ^ Markoff, John (10 April 2008). "Microsoft Introduces Tool for Avoiding Traffic Jams". New York Times. Retrieved 16 March 2011. 
  8. ^ Markoff, John (17 July 2000). "Microsoft Sees Software 'Agent' as Way to Avoid Distractions". New York Times. Retrieved 16 March 2011. 
  9. ^ Lohr, Steve, and Markoff, John (24 June 2010). "Smarter Than You Think: Computers Learn to Listen, and Some Talk Back". New York Times. Retrieved 12 March 2011. 
  10. ^ Waldrop, M. Mitchell (March–April 2008). "TR10: Modeling Surprise". Technology Review. Retrieved 12 March 2011. 

External links[edit]