Arkadi S. Nemirovski

Biosketch

Arkadi Nemirovski is applied mathematician recognized for his work on convex optimization (complexity, polynomial time and first order algorithms, conic programming, robust optimization) and nonparametric statistics. Nemirovski was born and grew up in Moscow, Russia; he got his degrees in Mathematics (M.Sc., 1970; Ph.D., 1974) from Moscow State University. In 1993, he moved to Israel, where in 1993-2006 he was full professor at the Technion – Israel Institute of Technology. Since 2005, he is Professor and John P. Hunter Jr. Academic Chair in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Nemirovski was awarded the 1982 Fulkerson Prize (with L. Khaciyan and D. Yudin) of the Mathematical Programming Society and the AMS, the 1991 Dantzig Prize (with M. Grotschel) of MPS and SIAM, the 2003 John von Neumann Theory Prize (with M. Todd) of INFORMS, and the 2019 Norbert Wiener Prize in Applied Mathematics (with M. Berger) of AMS and SIAM. He is a member of National Academy of Engineering (since 2017), American Academy of Arts and Sciences (since 2018), and National Academy of Sciences (2020).

Research Interests

Arkadi Nemirovski's research focuses on Convex Optimization Theory and Algorithms, with emphasis on (a) investigating complexity of and developing provably efficient algorithms for nonlinear convex problems, (b) optimization under uncertainty, (c) applications of convex optimization in engineering, and (d) nonparametric statistics. Jointly with D. Yudin, he invented the Ellipsoid algorithm and made formative contributions to information-based complexity theory of convex programming. Jointly with Yu. Nesterov, he developed general theory of polynomial time interior point algorithms for nonlinear convex problems, including conic ones. Other algorithmic contributions of Nemirovski include discovery and development of deterministic and stochastic Mirror Descent. His joint research with A. Ben-Tal contributed significantly to emerging and progress of Robust Optimization methodology for optimization under uncertainty. Recently, he, jointly with A. Iouditski, works on applications of convex programming theory to design of statistically efficient inferences in high dimensional statistics.