selected publications academic article Eckman, D. J., Henderson, S. G., & Shashaani, S. (2023). Diagnostic Tools for Evaluating and Comparing Simulation- Optimization Algorithms. INFORMS Journal on Computing. 35(2), 350-367. Eckman, D. J., Henderson, S. G., & Shashaani, S. (2023). SimOpt: A Testbed for Simulation-Optimization Experiments. INFORMS Journal on Computing. 35(2), 495-508. Eckman, D. J., & Henderson, S. G. (2022). Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures. INFORMS Journal on Computing. 34(3), 1711-1728. Eckman, D. J., Plumlee, M., & Nelson, B. L. (2022). Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces. Operations Research. 70(6), 3473-3489. Eckman, D. J., & Henderson, S. G. (2021). Fixed-Confidence, Fixed-Tolerance Guarantees for Ranking-and-Selection Procedures. ACM Transactions on Modeling and Computer Simulation. 31(2), 1-33. Eckman, D. J., & Henderson, S. G. (2018). Reusing Search Data in Ranking and Selection: What Could Possibly Go Wrong?. ACM Transactions on Modeling and Computer Simulation. 28(3), 18-15. Eckman, D. J., Maillart, L. M., & Schaefer, A. J. (2016). Optimal pinging frequencies in the search for an immobile beacon. IIE Transactions. 48(6), 489-500. Shashaani, S., Eckman, D., & Sanchez, S. Data Farming the Parameters of Simulation-Optimization Solvers. ACM Transactions on Modeling and Computer Simulation. conference paper Ford, M. T., Henderson, S. G., & Eckman, D. J. (2022). Automatic Differentiation for Gradient Estimators in Simulation. 3134-3145. Eckman, D. J., Plumlee, M., & Nelson, B. L. (2021). Flat Chance! Using Stochastic Gradient Estimators to Assess Plausible Optimality for Convex Functions. 1-12. Eckman, D. J., & Henderson, S. G. (2020). Biased Gradient Estimators in Simulation Optimization. 2935-2946. Eckman, D. J., Plumlee, M., & Nelson, B. L. (2020). Revisiting Subset Selection. 2972-2983. Eckman, D. J., Henderson, S. G., & Pasupathy, R. (2019). Redesigning a Testbed of Simulation-Optimization Problems and Solvers for Experimental Comparisons. 3457-3467. Eckman, D. J., & Henderson, S. G. (2018). GUARANTEES ON THE PROBABILITY OF GOOD SELECTION. 351-365. Eckman, D. J., & Feng, M. B. (2018). Green Simulation Optimization Using Likelihood Ratio Estimators. 2049-2060. Dong, N., Eckman, D. J., Zhao, X., Henderson, S. G., & Poloczek, M. (2017). Empirically Comparing the Finite-Time Performance of Simulation-Optimization Algorithms. 2206-2217. Eckman, D. J., & Henderson, S. G. (2016). Challenges in Applying Ranking and Selection After Search. 3684-3685. Bountourelis, T., Eckman, D., Luangkesorn, L., Schaefer, A., Nabors, S. G., & Clermont, G. (2012). SENSITIVITY ANALYSIS OF AN ICU SIMULATION MODEL. 1-12. institutional repository document Dong, N., Eckman, D. J., Poloczek, M., Zhao, X., & Henderson, S. G. (2017). Comparing the Finite-Time Performance of Simulation-Optimization Algorithms
principal investigator on Collaborative Research: Inference on expensive, grey-box simulation models awarded by National Science Foundation - (Arlington, Virginia, United States) 2022 - 2025
teaching activities ISEN355 System Simulation Instructor ISEN413 Advanced Data Analytics Instructor ISEN491 Hnr-research Instructor ISEN613 Engr Data Analysis Instructor ISEN625 Simulatn Methods & App Instructor ISEN689 Sptp: Monte Carlo Methods Instructor ISEN691 Research Instructor
education and training Ph.D. in Operations Research, Cornell University - (Ithaca, New York, United States) 2019 Northwestern University - (Evanston, Illinois, United States) , Postdoctoral Training 2021