Research Areas
Stochastic Analysis & Simulation

Simulation research derives new methods for the design, analysis, and optimization of simulation experiments. Research on stochastic models develops and analyzes models of systems with random behavior. Researchers then apply those methods and models in production, logistics, and financial engineering domains.

Research Themes

Central themes of our research program are:

  • Quantifying uncertainty
  • Measuring and controlling risk
  • Developing robust solution algorithms
  • Analysis of queueing systems

Student Coursework

Students working in simulation prepare by undertaking rigorous training in stochastic processes, statistics, and optimization.

Students also obtain a solid grounding in the application domain of interest, often by working with an industrial sponsor.


Recent PhD graduates have gone on to:

  • Derive robust simulation optimization algorithms
  • Invent methods to precisely estimate critical financial risk measures
  • Provide tools to represent large-scale industrial simulations as easy-to-use metamodels


Faculty members in stochastic analysis and simulation include: