Thomas G. Dietterich is a pioneering computer scientist and one of the founders of the field of machine learning who currently holds the position of Distinguished Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University. He joined the university faculty in 1985 after completing his Ph.D. at Stanford University in 1984 and was named Distinguished Professor in 2013, representing the highest honor for faculty at the institution. Throughout his distinguished career, he has held numerous leadership positions including President of the Association for the Advancement of Artificial Intelligence from 2014 to 2016 and founding President of the International Machine Learning Society. His early career included research positions at the University of Illinois and Stanford University, establishing him as a leading figure in artificial intelligence research from the outset of his professional journey.
Dietterich's groundbreaking research has fundamentally shaped the development of machine learning as a scientific discipline through seminal contributions including the application of error-correcting output coding to multiclass classification, the formalization of the multiple-instance problem, and the development of the MAXQ framework for hierarchical reinforcement learning. His innovative work on integrating non-parametric regression trees into probabilistic graphical models has enabled more robust and interpretable machine learning systems across numerous practical applications. These theoretical contributions have been widely adopted throughout the field and have influenced generations of researchers and practitioners working in artificial intelligence. Over his prolific career, he has secured more than $30 million in research funding and authored over 200 refereed publications that have significantly advanced the theoretical foundations of machine learning.
Beyond his direct research contributions, Dietterich has played a pivotal role in building the machine learning community through co-founding the Journal of Machine Learning Research and serving as Executive Editor of the Machine Learning journal from 1992 to 1998. He continues to influence the field through his current research on robust artificial intelligence, anomaly detection, and applications in computational sustainability, addressing critical challenges in ecosystem management and sustainable development. His leadership extends to numerous advisory roles including service on the DARPA ISAT Steering Committee and as moderator for the machine learning category on arXiv since 2007. Dietterich remains committed to mentoring the next generation of researchers and fostering interdisciplinary collaborations that bridge computer science with ecological science to tackle pressing environmental challenges.
