Arkadi Nemirovski

Arkadi Nemirovski is the John P. Hunter, Jr. Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech. He holds a Ph.D. in Mathematics (1974) from Moscow State University, a Doctor of Sciences in Mathematics (1990) from the USSR Supreme Attestation Board, and an honorary Doctor of Mathematics from the University of Waterloo (2009).

• Ph.D. in Mathematics, Moscow State University (1974)
• Doctor of Sciences in Mathematics, USSR Supreme Attestation Board (1990)
• Doctor of Mathematics (Honoris Causa), University of Waterloo (2009)

His research focuses on Optimization Theory and Algorithms, with emphasis on complexity analysis, efficient methods for nonlinear convex programs, robust optimization, optimization under uncertainty, and applications in engineering and nonparametric statistics. He has pioneered advancements in interior-point methods, semidefinite programming, and stochastic approximation, shaping modern convex optimization.

His article trends highlight a trajectory from foundational interior-point algorithms (1990s) to robust optimization (2000s) and recent works on first-order methods, polyhedral estimates, and applications in machine learning, signal processing, and tomography. Key subfields include matrix norms, large-scale optimization, and stochastic uncertainty handling.

Scientific awards include:

• 1982 Fulkerson Prize (joint with L. Khachiyan and D. Yudin)
• 1991 Dantzig Prize (joint with M. Grotschel)
• 2003 John von Neumann Theory Prize (joint with M. Todd)
• 2017 Member, National Academy of Engineering
• 2018 Fellow, American Academy of Arts and Sciences
• 2020 Norbert Wiener Prize (joint with M. Berger)

He has supervised students like Dmitry Gabelev (polynomial-time cutting plane algorithms), Daureen Steinberg (matrix norms in robust optimization), and Eitan Rubinstein (SVMs via advanced optimization), with their works later formalized in academic journals.