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Game-Theory based Mathematical Optimization under Uncertainty and Distributional Ambiguity: Applications to Security
Operations Research, Game Theory, Data-Driven Optimization, Decision Making under Uncertainty Methodology: Stochastic and Distributionally Robust Integer Programming, Combinatorial Optimization, and Location Science.
Bansal is an Associate Professor and a Grado Early Career Faculty Fellow with Virginia Tech’s Grado Department of Industrial and Systems Engineering.
He has a bachelors in Electrical Engineering, and M.S. in industrial engineering, and a Ph.D. in operations research.
Prior to joining Virginia Tech, he was a postdoctoral fellow in the Department of Industrial Engineering and Management Sciences at Northwestern University.
His research is focused on the theory of mixed integer programming, stochastic and distributionally robust optimization, game theory, and location science along with their applications in homeland security, logistics, and supply chain management.
He has received multiple grants from National Science Foundation, Department of Defense, and Cyber Commonwealth Initiatives.
He has served as president of INFORMS Junior Faculty Interest Group and VT's Engineering Faculty Organization.
He has published papers in such journals as SIAM Journal on Optimization, Mathematical Programming, European Journal on Operations Research, Discrete Applied Mathematics, Networks, and IEEE Transaction on Automation Science & Engineering.
Texas A&M University