Operations Research

Operations research combines the use of optimization, probability and statistics to solve problems in contextual domains such as business, energy systems, health services, financial services, telecommunications and transportation. Active areas of research often work at the intersection of these disciplines, such as the use of optimization in the estimation of large scale statistical models, optimal collection of information, and stochastic optimization.

Research Area Faculty

Amir Ali Ahmadi

Research Interests: Optimization, dynamical systems, learning for dynamics and control, computational complexity. Applications of these disciplines to optimization problems in systems theory, portfolio management, machine learning, and robotics.

Alain Kornhauser

Research Interests: Autonomous Vehicles, SmartDrivingCars, the fundamental design of computer vision techniques for for the rapid classification and identification of the driving environment, analysis and classification of collision-free driving scenarios, quantification of accident risk and the investigation, formulation…

William Massey
Edwin S. Wilsey Professor

Research Interests: Stochastic networks and queueing theory, performance models for service systems, dynamic optimal control and pricing for service systems, stochastic analysis and asymptotics, stochastic dominance on partially ordered spaces, theory of dynamic rate queues, special functions; communications, healthcare

John Mulvey

Research Interests: Financial optimization models and associated algorithms, asset management strategies for global pension plans, (re)insurance companies, and other long-term investors, dynamic portfolio tactics for hedge funds, replicating performance of active managers

Bartolomeo Stellato
Assistant Professor

Research Interests: Data-driven computational tools for mathematical optimization, machine learning and optimal control. Real-time and embedded optimization. Dynamical systems and optimization-based control. Differentiable optimization. First-order methods for large scale optimization. Machine learning for optimization and…

Robert Vanderbei

Research Interests: Interior-point methods for linear, convex, nonconvex, semidefinite optimization, robust optimization, the parametric simplex method, and constraint matrix sparsification (e.g. fast Fourier optimization). Applications include high-contrast imaging (to design a NASA space telescope), finding new stable…