• Diploma in Mathematics University of Duisburg-Essen, Germany, 2003
  • Ph.D. in Mathematics University of Duisburg-Essen, Germany, 2005
  • Postdoctoral Researcher Massachusetts Institute of Technology, 2006

Honors & Awards.

  • David M. McKenney Family Early Career Professorship, 2016
    in the H. Milton School of Industrial and Systems Engineering at Georgia Tech
  • CAREER Award, National Science Foundation, 2015
  • EGRIE Annual Meeting Best paper award, 2014
    for “Convergence of Capital and Insurance Markets: Pricing Aspects of Index-Linked Catastrophic Loss Instruments”, N. Gatzert, S. Pokutta, and N. Vogl
  • Coca Cola Early Career Professorship, 2014
    in the H. Milton School of Industrial and Systems Engineering at Georgia Tech
  • Symposium on Theory of Computing (STOC) Best paper award, 2012
    for “Linear vs. Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds”, S. Fiorini, S. Massar, S. Pokutta, H.R. Tiwary, R. de Wolf
  • Energy-Finance Best paper award, 2010
    for “On clearing coupled day-ahead electricity markets”, A. Martin, J. Müller, S. Pokutta
  • DAAD Postdoctoral fellow, 2006-2007
  • GIF Postdoctoral fellowship, 2006


Sebastian Pokutta received both his master’s degree in 2003 and his Ph.D. in 2005 in Mathematics from the University of Duisburg-Essen in Germany. Subsequent to his graduate studies he worked as a postdoctoral fellow at the MIT Operations Research Center. Upon completion Pokutta was appointed as an optimization specialist at IBM ILOG and later joined KDB Krall Demmel Baumgarten, a risk management consultancy. He then returned to academia as a research scientist at the Technische Universität Darmstadt and was a visiting lecturer at MIT. Prior to joining Georgia Tech, Pokutta worked as a professor at the University of Erlangen-Nürnberg.

Pokutta’s research is situated at the intersection of machine learning and optimization. A particular focus is on combining machine learning with discrete optimization techniques as well as the theory of extended formulations, exploring the limits of computation in alternative models of complexity both in optimization and machine learning. Pokutta has also worked on applications of optimization and machine learning, leveraging data in the context of pressing industrial and financial challenges. These areas include supply chain management, manufacturing, cyber-physical systems (incl. industrial internet, industry 4.0, internet of things), and finance. Examples of Pokutta’s applied work include stowage optimization problems for inland vessels, oil production problems, clearing of electricity markets, order fulfillment problems, warehouse location problems, simulation of autonomous vehicle fleets, portfolio optimization problems, and optimal liquidity management strategies.