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.
Plausible Inference Methods
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.
Methods of Plausible Inference for Multi-Objective Simulation Optimization
Zhao, J. and D. J. Eckman
Under review.
Slides / Poster
One-Shot Screening of Simulated Systems for Acceptability
Zhao, J. and D. J. Eckman
Under review.
Plausible Intervals: Global Inference from Limited Simulation of Structured Problems
Qiao, T., D. J. Eckman, and B. L. Nelson
ACM Transactions on Modeling and Computer Simulation (TOMACS). 2026. 36(2):7:1–7:24.
DOI / Paper / Slides / Slides
Methods of Plausible Inference: The Definitive Cookbook
Zhao, J., G. Keslin, D. J. Eckman, and B. L. Nelson
Proceedings of the 2025 Winter Simulation Conference. 2025. 88–102.
DOI / Paper / Slides
Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces
Eckman, D. J., M. Plumlee, and B. L. Nelson
Operations Research. 2022. 70(6): 3473–3489.
* Awarded 2023 Outstanding Simulation Publication (INFORMS Simulation Society).
DOI / Pre-Print / Slides / Presentation / GitHub
Flat Chance! Using Stochastic Gradient Estimators to Assess Plausible Optimality for Convex Functions
Eckman, D. J., M. Plumlee, and B. L. Nelson
Proceedings of the 2021 Winter Simulation Conference. 2021. Article No. 247.
DOI / Paper / Slides
Revisiting Subset Selection
Eckman, D. J., M. Plumlee, and B. L. Nelson
Proceedings of the 2020 Winter Simulation Conference. 2020. 2972-2983.
DOI / Paper / Slides / Presentation
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 been driven through SimOpt – a growing testbed of SO problems and solvers – and includes the development of new experiment designs and analysis techniques for understanding the behavior of SO algorithms.
Large Language Models Can Invent Solvers Autonomously
Cen, W, D. J. Eckman, S. G. Henderson, and S. Shashaani
Accepted to the 2026 Winter Simulation Conference.
Diagnostic Tools for Evaluating Solvers for Stochastically Constrained Simulation Optimization Problems
Felice, N., D. J. Eckman, S. G. Henderson, and S. Shashaani
Accepted to the 2026 Winter Simulation Conference.
Generators for Large-Scale Stochastic Simulation-Optimization Experiments
Oswalt, R. and D. J. Eckman
Proceedings of SW25: The OR Society’s 12th Simulation Workshop. 2025. 229–238.
DOI / Slides
Repeatedly Solving Similar Simulation-Optimization Problems: Insights From Data Farming
Felice, N., S. Shashaani, D. J. Eckman, and S. M. Sanchez
Proceedings of the 2024 Winter Simulation Conference. 2024. 3470–3481.
DOI / Paper
Data Farming the Parameters of Simulation-Optimization Solvers
Shashaani, S., D. J. Eckman, and S. M. Sanchez
ACM Transactions on Modeling and Computer Simulation (TOMACS). 2024. 34(4):24:1–24:29.
DOI
Stochastic Constraints: How Feasible is Feasible?
Eckman, D. J., S. G. Henderson, and S. Shashaani
Proceedings of the 2023 Winter Simulation Conference. 2023. 3589-3600.
DOI / Paper / Slides
SimOpt: A Testbed for Simulation-Optimization Experiments
Eckman, D. J., S. G. Henderson, and S. Shashaani
INFORMS Journal on Computing. 2023. 35(2):495–508.
DOI / Pre-Print / GitHub
Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms
Eckman, D. J., S. G. Henderson, and S. Shashaani
INFORMS Journal on Computing. 2023. 35(2):350–367.
DOI / Pre-Print
Redesigning a Testbed of Simulation-Optimization Problems and Solvers for Experimental Comparisons
Eckman, D. J., S. G. Henderson, and R. Pasupathy
Proceedings of the 2019 Winter Simulation Conference. 2019. 3457–3467.
DOI / Paper / Slides
Empirically Comparing the Finite-Time Performance of Simulation-Optimization Algorithms
Dong, N., D. J. Eckman, X. Zhao, M. Poloczek, and S. G. Henderson
Proceedings of the 2017 Winter Simulation Conference. 2017. 2206–2217.
DOI / Paper / Slides
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. A primary goal of this line of research is to discover new design principles for general-purpose R&S procedures.
Rate-Optimal Budget Allocation for the Probability of Good Selection
Kim, T. and D. J. Eckman
Proceedings of the 2024 Winter Simulation Conference. 2024. 3324–3335.
* Finalist for Best Contributed Theoretical Paper.
Paper / Slides
Screening Simulated Systems for Optimization
Zhao, J., J. Gatica, and D. J. Eckman
Proceedings of the 2023 Winter Simulation Conference. 2023. 1-15.
DOI / Paper / Slides
Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures
Eckman, D. J. and S. G. Henderson
INFORMS Journal on Computing. 2022. 34(3):1711–1728.
DOI / Paper / Slides / Presentation / GitHub
Fixed-Confidence, Fixed-Tolerance Guarantees for Selection-of-the-Best Procedures
Eckman, D. J. and S. G. Henderson
ACM Transactions on Modeling and Computer Simulation (TOMACS). 2021. 31(2):7:1–7:33.
DOI / Pre-Print / Slides
Guarantees on the Probability of Good Selection
Eckman, D. J. and S. G. Henderson
Proceedings of the 2018 Winter Simulation Conference. 2018. 351–365.
DOI / Paper / Slides
Reusing Search Data in Ranking and Selection: What Could Possibly Go Wrong?
Eckman, D. J. and S. G. Henderson
ACM Transactions on Modeling and Computer Simulation (TOMACS). 2018. 28(3):18:1–18:15.
DOI / Pre-Print / Slides / Poster / GitHub
Simulation Analytics
This line of research studies ways to integrate simulation modeling and statistical learning for input and output analysis, including uncertainty quantification, validation, prediction, and optimization. For example, I am studying how statistical learning techniques can be applied on time-dependent trace data that describe changes to the state of the real or simulated system over time for better prediction and control in digital twins.
Subtrace-Conditional Validation of Simulation Models and Digital Twins
Ghasemloo, M., D. J. Eckman, and Y. Li
Accepted to the 2026 Winter Simulation Conference.
Quantifying and Attributing Submodel Uncertainty in Stochastic Simulation Models and Digital Twins
Ghasemloo, M., D. J. Eckman, and Y. Li
Under review.
Pre-print
Accelerating Reinforcement Learning Training Using Simulation Surrogate Models
Ghasemloo, M., D. J. Eckman, and Y. Li
Proceedings of the IISE Annual Conference & Expo 2026.
* Runner-Up for Best Student Paper in Modeling & Simulation Track.
Pre-print
An Agglomerative Clustering Algorithm for Simulation Output Distributions Using Regularized Wasserstein Distance
Ghasemloo, M. and D. J. Eckman
INFORMS Journal on Data Science. 2026. 5(1):65-80.
DOI / Slides
Quantifying Uncertainty from Machine Learning Surrogate Models Embedded in Simulation Models
Ghasemloo, M., D. J. Eckman, and Y. Li
Proceedings of the 2025 Winter Simulation Conference. 2025. 3418–3429.
Paper / Slides
Miscellaneous
An Integrated Optimization-Simulation Framework for Zone-Based Hurricane Evacuation Planning
Wu, Y., D. J. Eckman, and X. Nie
Under review.
Poster
Automatic Differentiation for Gradient Estimators in Simulation
Ford, M. T., D. J. Eckman, and S. G. Henderson
Proceedings of the 2022 Winter Simulation Conference. 2022. 3134–3145.
DOI / Paper
Biased Gradient Estimators in Simulation Optimization
Eckman, D. J. and S. G. Henderson
Proceedings of the 2020 Winter Simulation Conference. 2020. 2935-2946.
DOI / Paper
Green Simulation Optimization Using Likelihood Ratio Estimators
Eckman, D. J. and M. B. Feng
Proceedings of the 2018 Winter Simulation Conference. 2018. 2049–2060.
* Awarded Best Student Paper (INFORMS Simulation Society).
DOI / Paper / Slides / Poster
Optimal Pinging Frequencies in the Search for an Immobile Beacon
Eckman, D., L. Maillart, and A. Schaefer
IIE Transactions. 2016. 48(6):489–500.
* Awarded Best Applications Paper in Operations Engineering & Analytics Issue.
DOI / Pre-Print / Slides
Sensitivity Analysis of an ICU Simulation Model
Bountourelis, T., D. Eckman, L. Luangkesorn, A. Schaefer, S. G. Nabors, and G. Clermont
Proceedings of the 2012 Winter Simulation Conference. 2012. 931–942.
DOI / Paper