Shabbir Ahmed awarded the 2018 INFORMS Optimization Society Farkas Prize

Shabbir Ahmed has been awarded the 2018 Farkas Prize of the INFORMS Optimization Society. This prize was established in 2006 and is awarded annually at the Fall INFORMS Annual Meeting to a mid-career researcher for outstanding contributions to the field of optimization, over the course of their career. The award includes a cash amount of $1,000 and a citation certificate. Shabbir will present his work at the INFORMS Annual Meeting in Phoenix. The citation reads:

The 2018 Farkas Prize of the INFORMS Optimization Society is awarded to Shabbir Ahmed, Anderson-Interface Chair, and Professor, H. Milton Stewart School of Industrial & Systems Engineering at the Georgia Institute of Technology, for his fundamental contributions to the theory and practice of stochastic discrete optimization.

Shabbir Ahmed received his Ph.D. from the University of Illinois in 2000, and his dissertation on stochastic integer programming won the Dantzig Prize from INFORMS. Since then, he has become a recognized worldwide leader in the integration of two challenging methodologies – stochastic programming and integer programming – essential for solving important optimization problems in energy, supply chain, transportation, and finance.

Professor Ahmed's theoretical contributions to stochastic programming have been broad and deep. These have improved the understanding of multistage stochastic programming and consistent formulations of risk preferences and provided bounds on sample average approximation solutions to non-convex chance-constrained optimization problems. His contributions to mathematical programming computation address some of the most difficult but important topics in the field with wide applicability in production systems, energy systems, healthcare, transportation, security, and more. His ability to exploit integer-programming structures that arise in stochastic programming is a recurring theme in his research. Of particular importance is: (i) his work that allows combining of single-scenario mixed-integer programming inequalities in a multi-stage stochastic program; (ii) his research on using integer programming, with knapsack inequalities, to solve a class of probabilistically constrained linear programs; and, (iii) his recent work on extending decomposition algorithms for solving large-scale multi-stage stochastic integer programs.