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.