My research focuses on the use of stochastic simulation for decision-making under uncertainty. This encompasses everything from the design and analysis of ranking-and-selection (R&S) procedures to the comparison of simulation-optimization (SO) algorithms to the development of new methods for simulation output analysis.
Exploiting Structure in SO
In certain cases, SO problems possess structural properties that can be verified analytically, e.g., a bounded, Lipschitz-continuous objective function. I am studying ways to exploit such functional information to deliver statistical inference (e.g., confidence regions, screening), even at unsimulated systems. This set of ideas is called Plausible Inference.
Benchmarking SO Algorithms
Compared to deterministic optimization algorithms, SO algorithms present additional challenges when it comes to benchmarking. I am exploring ways to evaluate and compare the finite-time performance of SO algorithms. This effort has led to a major redesign of SimOpt – a growing testbed of SO problems and solvers – and the development of new experiment designs and analysis techniques for understanding the behavior of SO algorithms.
Ranking & Selection
Ranking-and-selection procedures select from one or more simulated alternatives from among a finite set and can provide either frequentist or Bayesian statistical guarantees. I am examining the interplay between the design of R&S procedures and the guarantees they deliver, including for SO problems with stochastic constraints and multiple objectives.
Simulation Analytics
A single replication of a discrete-event simulation model produces copious output data describing changes to the state of the system over time. I am studying how statistical learning techniques can be applied on this (time-dependent) trace data to yield valuable insights into how the simulated system behaves that can be used for better prediction and control.