Thomas G. Dietterich: Research Bio

How can computers be programmed so that they can learn from their experience and become more effective over time? How can high-performance software systems be constructed from data? How can the vast quantities of scientific and business data be analyzed to find patterns and suggest hypotheses? These are the questions studied by Thomas Dietterich and his research group. Among the theoretical and practical results of this work have been (a) error-correcting output coding (a method for supervised learning over many categories), (b) bias/variance analysis of classification problems, (c) methods for solving multiple-instance learning problems (with applications to pharmaceutical development), (d) a methodology for applying reinforcement learning to solve industrial combinatorial optimization problems, and (e) the MAXQ architecture for hierarchical reinforcement learning. Dr. Dietterich's current research projects include (a) flexible machine learning methods for fitting species distribution models for thousands of species, (b) machine learning methods for fitting hidden process models to ecological data (including continent-scale modeling of bird migration), (c) algorithms for (approximately) solving large spatio-temporal Markov Decision Programs that arise in ecosystem management problems, (d) computer vision methods for understanding subparts and structures in biological specimens (with applications to automated scoring of phenotypes), and (e) anomaly detection methods with applications to automated cleaning of environmental data, automatic detection of novel objects, detection of security threats in computer systems, and robust behavior of AI systems when confronted with unknown unknowns.

Dr. Dietterich's currently pursues interdisciplinary research at the boundary of computer science, ecology, and sustainability policy. He is part of the leadership team for OSU's Ecosystem Informatics programs including the NSF Summer Institute in Ecoinformatics.

Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Distinguished Professor and Director of Intelligent Systems in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. In 1987, he was named a Presidential Young Investigator for the NSF. In 1990, he published, with Dr. Jude Shavlik, the book entitled Readings in Machine Learning, and he also served as the Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90). From 1992-1998 he held the position of Executive Editor of the journal Machine Learning. The Association for the Advancement of Artificial Intelligence named him a Fellow in 1994, and the Association for Computing Machinery did the same in 2003. In 2000, he co-founded a free electronic journal: The Journal of Machine Learning Research, and he is currently a member of the Editorial Board. Since 2007, he has served as arXiv moderator for Machine Learning. He was Technical Program Chair of the Neural Information Processing Systems (NIPS) conference in 2000 and General Chair in 2001. He is Past-President of the International Machine Learning Society, a member of the IMLS Board, and he also serves on the Advisory Board of the NIPS Foundation. He is President of the Association for the Advancement of Artificial Intelligence.