Prof. Dimitri Bertsekas
Chief Scientific Advisor
Dimitri Bertsekas’ undergraduate studies were in engineering at the National Technical University of Athens, Greece. He obtained his MS in electrical engineering at the George Washington University, Wash. DC in 1969, and his Ph.D. in system science in 1971 at the Massachusetts Institute of Technology (M.I.T.).
Dr. Bertsekas has held faculty positions with the Engineering-Economic Systems Dept., Stanford University (1971-1974) and the Electrical Engineering Dept. of the University of Illinois, Urbana (1974-1979). From 1979 to 2019 he was with the Electrical Engineering and Computer Science Department of M.I.T., where he served as McAfee Professor of Engineering. In 2019, he was appointed Fulton Professor of Computational Decision Making, and a full time faculty member at the School of Computing and Augmented Intelligence at Arizona State University (ASU), Tempe. In 2023 he was appointed Chief Scientific Advisor of Bayforest Technologies, a London-based quantitative investment company.
Professor Bertsekas’ research spans several fields, including optimization, control, and large-scale computation, and is closely tied to his teaching and book authoring activities. He has written numerous research papers, and nineteen books and research monographs, several of which are used as textbooks in MIT and ASU classes.
Professor Bertsekas was awarded the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for his book “Neuro-Dynamic Programming”, the 2001 ACC John R. Ragazzini Education Award, the 2009 INFORMS Expository Writing Award, the 2014 ACC Richard E. Bellman Control Heritage Award for “contributions to the foundations of deterministic and stochastic optimization-based methods in systems and control,” the 2014 Khachiyan Prize for Life-Time Accomplishments in Optimization, the SIAM/MOS 2015 George B. Dantzig Prize, and the 2022 IEEE Control Systems Award. Together with his coauthor John Tsitsiklis, he was awarded the 2018 INFORMS John von Neumann Theory Prize, for the contributions of the research monographs “Parallel and Distributed Computation” and “Neuro-Dynamic Programming”. In 2001, he was elected to the United States National Academy of Engineering for “pioneering contributions to fundamental research, practice and education of optimization/control theory, and especially its application to data communication networks.”
Dr. Bertsekas’ recent books are “Introduction to Probability: 2nd Edition” (2008), “Convex Optimization Theory” (2009), “Dynamic Programming and Optimal Control,” Vol. I, (2017), and Vol. II: (2012), “Convex Optimization Algorithms” (2015), “Reinforcement Learning and Optimal Control” (2019), “Rollout, Policy Iteration, Distributed Reinforcement Learning” (2020), “Abstract Dynamic Programming” (2022, 3rd edition), and “Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control” (2022), all published by Athena Scientific.