Archive of Author | Thomas Dietterich

Dr. Thomas Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor Emeritus and Director of Intelligent Systems Research in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. Dietterich is one of the pioneers of the field of Machine Learning and has authored more than 190 refereed publications and two books. His research is motivated by challenging real world problems with a special focus on ecological science, ecosystem management, and sustainable development.  He is best known for his work on ensemble methods in machine learning including the development of error-correcting output coding. Dietterich has also invented important reinforcement learning algorithms including the MAXQ method for hierarchical reinforcement learning.

Dietterich has devoted many years of service to the research community. He is Past President of the Association for the Advancement of Artificial Intelligence, and he previously served as the founding president of the International Machine Learning Society. Other major roles include Executive Editor of the journal Machine Learning, co-founder of the Journal for Machine Learning Research, and program chair of AAAI 1990 and NIPS 2000. Dietterich is a Fellow of the ACM, AAAI, and AAAS.

http://web.engr.oregonstate.edu/~tgd/

Articles with Thomas Dietterich


AI and Machine Learning

Thomas Dietterich is one of the pioneers of the field of Machine Learning. His research is motivated by challenging real world problems with a special focus on ecological science, ecosystem management, and sustainable development. He is Past President of the Association for the Advancement of Artificial Intelligence, and he previously served as the founding president of the International Machine Learning Society. In this exclusive interview he discusses his ideas and work.