Sponsored by the IEEE Control Systems Society
For fundamental contributions to the methodology of optimization and control of uncertain and large-scale dynamical systems, and their dissemination through outstanding monographs and textbooks
One of the first researchers to examine estimation and control of unknown-but-bounded uncertain systems, much of Dimitri P. Bertsekas’ work was ahead of its time but continues to impact current developments in optimization and control of large-scale systems. He provided a theoretical framework for stochastic dynamic programming extendable to uncountable state spaces that became the standard reference on the subject. He also reframed reinforcement learning as an approximate version of dynamic programming with results that eventually served as a unifying conceptual framework. Current research in distributed networking and machine learning relies on the foundations provided by his work on distributed routing algorithms for data networks, and his auction algorithm for network optimization is one of the fastest in practice.
An IEEE Fellow, Bertsekas is a professor with the Fulton School of Computing and Augmented Intelligence at Arizona State University, Tempe, AZ, USA.