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Optimization is a scientific approach to decision-making for complex systems. It utilizes mathematical models that are abstract representations of the actual system. Due to its versatility and wide ranging impact, optimization serves as a keystone in operations research and analytics.

Our research spans theory, the design of algorithms, computational innovation, and modeling for a wide range of applications. We seek to develop flexible, robust, and scalable techniques that can handle complex mathematical structures arising from real-world problems.



Complementarity and Equilibrium Constraints

Convex Optimization

Discrete and Combinatorial Optimization

Dynamic Optimization

Large-Scale Optimization

Network Optimization

Nonlinear Optimization

Optimization Under Uncertainty
  • Data-Driven Optimization

  • Markov Decision Processes

  • Robust Optimization

  • Stochastic Optimization

  • Simulation Optimization


Energy Systems
Logistics, Production, and Scheduling
Statistical Learning
Water Resource Management

Concentration Faculty

Affiliated Faculty