Michael L. Littman

American computer scientist
Michael L. Littman
Littman in 2023
Born (1966-08-30) August 30, 1966 (age 57)
Philadelphia, Pennsylvania
NationalityAmerican
Alma materBrown University
Yale University
AwardsAAAI Fellow
Scientific career
FieldsComputer Science
InstitutionsBrown University
Rutgers University
Georgia Institute of Technology
AT&T
Duke University
National Science Foundation
ThesisAlgorithms for sequential decision-making (1996)
Doctoral advisorLeslie P. Kaelbling
Websitecs.brown.edu/~mlittman/

Michael Lederman Littman (born August 30, 1966) is a computer scientist, researcher, educator, and author. His research interests focus on reinforcement learning. He is currently a University Professor of Computer Science at Brown University, where he has taught since 2012.

Career

Before graduate school, Littman worked with Thomas Landauer at Bellcore and was granted a patent for one of the earliest systems for cross-language information retrieval. Littman received his Ph.D. in computer science from Brown University in 1996. From 1996 to 1999, he was a professor at Duke University. During his time at Duke, he worked on an automated crossword solver PROVERB, which won an Outstanding Paper Award in 1999 from AAAI and competed in the American Crossword Puzzle Tournament. From 2000 to 2002, he worked at AT&T. From 2002 to 2012, he was a professor at Rutgers University; he chaired the department from 2009-12. In Summer 2012 he returned to Brown University as a full professor. He has also taught at Georgia Institute of Technology, where he was listed as an adjunct professor.[1] Littman is currently on rotation from Brown University as a Division Director at the National Science Foundation.[2]

Research

Littman's research interests are varied but have focused mostly on reinforcement learning and related fields, particularly, in machine learning more generally, game theory, computer networking, partially observable Markov decision process solving, computer solving of analogy problems and other areas. He is also interested in computing education more broadly and has authored a book on programming for everyone.[3]

Awards

  • Elected as an ACM Fellow in 2018 for "contributions to the design and analysis of sequential decision-making algorithms in artificial intelligence".[4]
  • Winner of the IFAAMAS Influential Paper Award (2014)
  • Winner of the AAAI “Shakey” Award for Overfitting: Machine Learning Music Video (2014)
  • Elected as a AAAI Fellow in 2010 for "significant contributions to the fields of reinforcement learning, decision making under uncertainty, and statistical language applications".[5]
  • Winner of the AAAI “Shakey” Award for Short Video for Aibo Ingenuity (2007)
  • Winner of the Warren I. Susman Award for Excellence in Teaching at Rutgers (2011)
  • Winner of the Robert B. Cox Award at Duke (1999)
  • Winner of the AAAI Outstanding Paper Award (1999)

References

  1. ^ "Michael Littman | College of Computing". www.cc.gatech.edu. Retrieved 2020-08-19.
  2. ^ "Michael Littman | NSF Division Director".
  3. ^ Code to Joy, MIT Press
  4. ^ 2018 ACM Fellows Honored for Pivotal Achievements that Underpin the Digital Age, Association for Computing Machinery, December 5, 2018
  5. ^ AAAI Fellows, Association for the Advancement of Artificial Intelligence

Bibliography

  • Littman, Michael L.; Sutton, Richard S.; Singh, Satinder (2002). "Predictive Representations of State" (PDF). Advances in Neural Information Processing Systems 14 (NIPS). pp. 1555–1561.
  • Littman, Michael L.; Keim, Greg A.; Shazeer, Noam M. (1999). "Solving crosswords with PROVERB". Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI). American Association for Artificial Intelligence. pp. 914–915.
  • Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research. 4: 237–285. doi:10.1613/jair.301.
  • Littman, Michael L. (1994). "Markov Games as a Framework for Multi-Agent Reinforcement Learning". International Conference on Machine Learning (ICML). pp. 157–163.

External links

Press references

  • Smart Home Programming: If-Then Statements Make A Comeback- Science 2.0
  • Computer Science for the Rest of Us- New York Times
  • Many Scientists Dismiss the Fear of Robots- Fortune
  • Celebrating the 20th Anniversary of MIME Email Attachments- NJ Tech Weekly
  • Humans Beat Poker Bot… Barely -NBC News
  • Duke Researchers Pit Computer Against Human Crossword Puzzle Players
  • Going Cruciverbalistic- American Scientist

Udacity Courses

  • Intro to Algorithms (over 88k student signups[citation needed])
  • Machine Learning (over 83k student signups[citation needed])
  • Reinforcement Learning and Decision Making
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International
  • ISNI
  • VIAF
National
  • Israel
  • United States
  • Czech Republic
Academics
  • Association for Computing Machinery
  • DBLP
  • Google Scholar
  • MathSciNet
  • Mathematics Genealogy Project
  • ORCID
  • zbMATH