Peter Bartlett is a professor in the Computer Science Division and Department of Statistics and Associate Director of the Simons Institute for the Theory of Computing at the University of California at Berkeley. His research interests include machine learning and statistical learning theory. He is the co-author, with Martin Anthony, of the book Neural Network Learning: Theoretical Foundations. He has served as an associate editor of the journals Bernoulli, Mathematics of Operations Research, the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, the IEEE Transactions on Information Theory, Machine Learning, and Mathematics of Control Signals and Systems, and as program committee co-chair for COLT and NIPS. He has consulted to a number of organizations, including General Electric, Telstra, SAC Capital Advisors, and Sentient. He was awarded the Malcolm McIntosh Prize for Physical Scientist of the Year in Australia in 2001, and was chosen as an Institute of Mathematical Statistics Medallion Lecturer in 2008, and an IMS Fellow and Australian Laureate Fellow in 2011. He was elected to the Australian Academy of Science in 2015.
Research Expertise and Interest
machine learning, statistical learning theory, adaptive control
August 25, 2020
The National Science Foundation (NSF) and Simons Foundation today (Aug. 25) awarded $10 million to a UC Berkeley-led program to gain a theoretical understanding of deep learning, which is making significant impacts across industry, commerce, science, and society.
August 10, 2020
The Simons Foundation has ensured a second decade of research and innovation for the Simons Institute for the Theory of Computing, based at UC Berkeley, through a $35.5 million grant. The grant, which will begin in 2022, after the conclusion of the Simons Institute's first 10 years, will support the Simons Institute's mission and activities through June 2032.