Professor of Computer Science and Statistics
Department of Statistics, Division of Computer Science/EECS
(510) 642-7780

Research Expertise and Interest

statistics, machine learning, statistical learning theory, adaptive control


Peter Bartlett is a professor in the Division of Computer Science and Department of Statistics. He is the co-author, with Martin Anthony, of the book Learning in Neural Networks: Theoretical Foundations, and has co-authored more than one hundred papers in the areas of machine learning and statistical learning theory. He has served as an associate editor of the journals Machine Learning, Mathematics of Control Signals and Systems, the Journal of Machine Learning Research, the Journal of Artificial Intelligence Research, and Foundations and Trends in Machine Learning, as a member of the editorial boards of Machine Learning and the Journal of Artificial Intelligence Research, and as a member of the steering committees of the Conference on Computational Learning Theory and the Algorithmic Learning Theory Workshop.

He has consulted to a number of corporations, including General Electric, Telstra, and SAC Capital Advisors. In 2001, he was awarded the Malcolm McIntosh Prize for Physical Scientist of the Year, for his work in statistical learning theory. He was a Miller Institute Visiting Research Professor in Statistics and Computer Science at U.C. Berkeley in Fall 2001, and a fellow, senior fellow and professor in the Research School of Information Sciences and Engineering at the Australian National University's Institute for Advanced Studies (1993-2003), and an honorary professor in the School of Information Technology and Electrical Engineering at the University of Queensland. He is a Fellow of the Institute of Mathematical Statistics. His research interests include machine learning, statistical learning theory, and adaptive control.

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