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.