Research

Research Interests

I am interested in stochastic modeling and simulation methodology, applied probability and statistics, and stochastic simulation experimental design and analysis. My recent research efforts include developing theory and methodology for simulation metamodeling, simulation-based input screening, sensitivity analysis, and optimization. I have also been working actively with collaborators to translate the methodological research results into analysis tools, which have been successfully applied in areas such as biomedical studies, power systems, and nanomaterial design.

Journal Articles

Metamodeling

Y. Zhang and X. Chen, "Sequential Metamodel-based Approaches to Level-set Estimation under Heteroscedasticity," Statistical Analysis and Data Mining: The ASA Data Science Journal, 17 (2024), e11697.
X. Chen, Y. Zhang, G. Xie, and J. Zhang “A Uniform Error Bound for Stochastic Kriging: Properties and Implications on Simulation Experimental Design,” ACM Transactions on Modeling and Computer Simulation.
G. Xie and X. Chen, “Efficient and Robust Online Trajectory Prediction for Unmanned Aerial Vehicles,” Journal of Aerospace Information Systems, 19 (2022), 143-153.
G. Xie, X. Chen, and Y. Weng, "Input Modeling and Uncertainty Quantification for Improving Volatile Residential Load Forecasting," Energy, 211 (2020), 119007.
M. Verma, J. Bassaganya-Riera, A. Leber, N. Tubau-Juni, S. Hoops, V. Abedi, X. Chen, and R. Hontecillas, "High-Resolution Computational Modeling of Immune Responses in the Gut," GigaScience, 8 (2019), giz062.
X. Chen, W. Wang, G. Xie, R. Hontecillas, M. Verma, A. Leber, J. Bassaganya-Riera, and V. Abedi, "Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter Pylori Infection via Spatio-Temporal Metamodeling," Frontiers in Applied Mathematics and Statistics-Mathematics of Computation and Data Science, 5:4 (2019).
W. Wang, N. Chen, X. Chen, and L. Yang, "A Variational Inference-Based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling," ACM Transactions on Modeling and Computer Simulation, 29 (2019), 6/1-6/22.
A. U. Khan, Y. Guo, X. Chen, and G. Liu, “Spectral-Selective Plasmonic Polymer Nanocomposites across the Visible and Near-Infrared,” ACS Nano, 13 (2019), 4255-4266.
G. Xie, X. Chen, and Y. Weng, "An Integrated Gaussian Process Modeling Framework for Residential Load Prediction," IEEE Transactions on Power Systems, 33 (2018), 7238-7248.
W. Wang and X. Chen, "An Adaptive Two-Stage Dual Metamodeling Approach for Stochastic Simulation Experiments," IISE Transactions, 50 (2018), 820-836.
D. Batur, J. M. Bekki, and X. Chen, "Quantile Regression Metamodeling: Toward Improved Responsiveness in the High-Tech Electronics Manufacturing Industry," European Journal of Operational Research, 264 (2018), 212-224.
X. Chen and Q. Zhou, "Sequential Design Strategies for Mean Response Surface Metamodeling via Stochastic Kriging with Adaptive Exploration and Exploitation," European Journal of Operational Research, 262 (2017), 575–585.
Y. Pei, F. Yang, X. Chen, N. Wu, and K. Wang, "Kriging-based Design of Experiments for Multi-Source Exposure-Response Studies in Nanotoxicology," ACS Sustainable Chemistry & Engineering, 5 (2017), 3223–3232.
X. Chen and K.-K. Kim, "Efficient VaR and CVaR Measurement via Stochastic Kriging," INFORMS Journal on Computing, 28 (2016), 629-644.
Z. Geng, F. Yang, X. Chen, and N. Wu, "Gaussian Process Based Modeling and Experimental Design for Sensor Calibration in Drifting Environments," Sensors & Actuators B: Chemical, 216 (2015), 321-331.
E. L. Boone, J. P. Brooks, E. Nyirabahizi, and X. Chen, "A Bayesian Model-Based Approach for Determining Multivariate Tolerable Regions," Journal of Environmental Statistics, 7 (2015).
X. Chen and K.-K. Kim, "Stochastic Kriging with Biased Sample Estimates," ACM Transactions on Modeling and Computer Simulation, 24 (2014), 8/1-8/23.
K. Wang, X. Chen, F. Yang, D. W. Porter, and N. Wu, "A New Stochastic Kriging Method for Modeling Multi-Source Exposure-Response Data in Toxicology Studies," ACS Sustainable Chemistry & Engineering, 2 (2014), 1581-1591.
X. Chen, B. E. Ankenman, and B. L. Nelson, "Enhancing Stochastic Kriging Metamodels with Gradient Estimators," Operations Research, 61 (2013), 512-528.
X. Chen, B. E. Ankenman, and B. L. Nelson, "The Effects of Common Random Numbers on Stochastic Kriging Metamodels," ACM Transactions on Modeling and Computer Simulation, 22 (2012), 7/1-7/20.

Factor screening and sensitivity analysis

W. Shi, X. Xie, and X. Chen, "Assessing and Mitigating the Impacts of Defect Complaints on Vehicle Recall Delays: An Integrated Agent-based Simulation and Factor Screening Approach," submitted.
W. Shi and X. Chen, "Cluster Sampling for Morris Method Made Easy," Naval Research Logistics, 68 (2021), 412–433.
G. Xie, X. Chen, and Y. Weng, "Input Modeling and Uncertainty Quantification for Improving Volatile Residential Load Forecasting," Energy, 211 (2020), 119007.
W. Shi, X. Chen, and J. Shang, "An Efficient Morris Method-based Framework for Simulation Factor Screening," INFORMS Journal on Computing, 31 (2019), 745–770.
W. Shi and X. Chen, "Controlled Morris Method: A New Factor-Screening Approach Empowered by a Distribution-Free Sequential Multiple Testing Procedure," Reliability Engineering and System Safety, 189 (2019), 299-314.
X. Chen, W. Wang, G. Xie, R. Hontecillas, M. Verma, A. Leber, J. Bassaganya-Riera, and V. Abedi, "Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter Pylori Infection via Spatio-Temporal Metamodeling," Frontiers in Applied Mathematics and Statistics-Mathematics of Computation and Data Science, 5:4 (2019).
M. Verma, J. Bassaganya-Riera, A. Leber, N. Tubau-Juni, S. Hoops, V. Abedi, X. Chen, and R. Hontecillas, "High-Resolution Computational Modeling of Immune Responses in the Gut," GigaScience, 8 (2019), giz062.
W. Shi and X. Chen, "Efficient Budget Allocation Strategies for Elementary Effects Method in Stochastic Simulation," Naval Research Logistics, 65 (2018), 218-241.

Adaptive sampling and analysis for stochastic systems

E. L. Boone, A. G. Abdel-Salam, I. Sahoo, R. Ghanam, X. Chen, and A. Hanif. “Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the State of Qatar,” Journal of Applied Statistics, 50 (2023), 231-246.
G. Xie and X. Chen, “Efficient and Robust Online Trajectory Prediction for Unmanned Aerial Vehicles,” Journal of Aerospace Information Systems, 19 (2022), 143-153.
G. Xie, X. Chen, and Y. Weng, "Enhance Load Forecastability: Optimize Data Sampling Policy by Reinforcing User Behaviors," European Journal of Operational Research, 295 (2021), 924-934.
G. Xie, X. Chen, and Y. Weng, "Input Modeling and Uncertainty Quantification for Improving Volatile Residential Load Forecasting," Energy, 211 (2020), 119007.
G. Xie, X. Chen, and Y. Weng, "An Integrated Gaussian Process Modeling Framework for Residential Load Prediction," IEEE Transactions on Power Systems, 33 (2018), 7238-7248.

Simulation optimization

W. Wang, H. Wan, and X. Chen, "Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection," Operations Research, forthcoming.

Conference Proceedings

J. Zhao and X. Chen, "Nested Heteroscedastic Gaussian Process for Simulation Metamodeling," Proceedings of the 2024 Winter Simulation Conference, accepted.
J. Wang, H. Wan, and X. Chen, "GANCQR: Estimating Prediction Intervals for Individual Treatment Effects with GANs," Proceedings of the 2024 Winter Simulation Conference, accepted.
J. Zhang, X. Chen, and R. Wang, "Asymptotic Normality of Joint Metamodel-based Sobol' Index Estimators," Proceedings of the 2023 Winter Simulation Conference, 3705-3716.
Y. Huang, H. Wan, and X. Chen, "Virtual Wearable Sensor Data Generation with Generative Adversarial Networks," Proceedings of the 2023 Winter Simulation Conference, 3729-3740.
Y. Zhang and X. Chen, “Empirical Uniform Bounds for Heteroscedastic Metamodeling,” Proceedings of the 2022 Winter Simulation Conference, 1-12.
Y. Zhang and X. Chen, “Information Consistency of Stochastic Kriging and Its Implications,” Proceedings of the 2021 Winter Simulation Conference, 1-12.
G. Xie and X. Chen, “Uniform Error Bounds for Stochastic Kriging,” Proceedings of the 2020 Winter Simulation Conference, 361-372.
W. Wang and X. Chen, "Distributed Variational Inference-Based Heteroscedastic Gaussian Process Metamodeling,'' Proceedings of the 2019 Winter Simulation Conference, 380-391.
G. Xie and X. Chen, "A Heteroscedastic t-Process Simulation Metamodeling Approach and Its Application in Inventory Control and Optimization,'' Proceedings of the 2017 Winter Simulation Conference, 3242-3253.
X. Chen, H. Sahar, and F. Yang, "Stochastic Co-Kriging for Steady-State Simulation Metamodeling," Proceedings of the 2017 Winter Simulation Conference, 1750-1761.
W. Shi and X. Chen, "Controlled Morris Method: A New Distribution-Free Sequential Testing Procedure for Factor Screening," Proceedings of the 2017 Winter Simulation Conference, 1820-1831.
G. Xie, X. Chen, and Y. Weng, "An Adaptive Communication Scheme for Bandwidth limited Residential Load Forecasting," Proceedings of the 49th North American Power Symposium.
W. Wang and X. Chen, "The Effects of Estimation of Heteroscedasticity on Stochastic Kriging," Proceedings of the 2016 Winter Simulation Conference, 326-337.
X. Chen and Q. Zhou, "Sequential Experimental Designs for Stochastic Kriging," Proceedings of the 2014 Winter Simulation Conference, 3821-3832.
J. M. Bekki, X. Chen, and D. Batur, "Steady-State Quantile Parameter Estimation: An Empirical Comparison of Stochastic Kriging and Quantile Regression," Proceedings of the 2014 Winter Simulation Conference, 3880-3891.
X. Chen and K.-K. Kim, "Building Metamodels for Quantile-Based Measures Using Sectioning," Proceedings of the 2013 Winter Simulation Conference, 521-532.
X. Chen, K. Wang, and F. Yang, "Stochastic Kriging with Qualitative Factors," Proceedings of the 2013 Winter Simulation Conference, 790-801.
X. Chen, K.-K. Kim , and B. L. Nelson, "Stochastic Kriging for Conditional Value-at-Risk and Its Sensitivities," Proceedings of the 2012 Winter Simulation Conference.
X. Chen, B. Ankenman, and B. L. Nelson, "Common Random Numbers and Stochastic Kriging," Proceedings of the 2010 Winter Simulation Conference, 947-956.

Presentations

"Uniform Error Bounds For Stochastic Kriging," the 2020 Winter Simulation Conference, December 2020.
"Uniform Error Bounds For Stochastic Kriging," the INFORMS Virtual 2020 Annual Meeting, November 2020.
"Distributed Variational Inference-Based Heteroscedastic Gaussian Process Metamodeling,'' the 2019 Winter Simulation Conference, Washington D.C., December 2019.
"Efficient Budget Allocation Strategies for Elementary Effects Method in Stochastic Simulation," the Sponsored Session: Learning through Intelligent Data Sampling at the INFORMS 2019 Annual Meeting, Seattle, WA, October 2019.
"An Efficient Morris Method-based Framework for Simulation Factor Screening," Katz Graduate School of Business, University of Pittsburgh, December 2018.
"An Efficient Morris Method-based Framework for Simulation Factor Screening," the INFORMS 2018 Annual Meeting, Phoenix, AZ, November 2018.
"A Variational Bayesian Inference-based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling," the Math Colloquium organized by the Dept. of Mathematics at Virginia Tech, February 2018.
"Controlled Morris Method: A New Distribution-Free Sequential Testing Procedure for Factor Screening," the 2017 Winter Simulation Conference, Las Vegas, NV, December 2017.
"Stochastic Co-Kriging for Steady-State Simulation Metamodeling, " the 2017 Winter Simulation Conference, Las Vegas, NV, December 2017.
"A Heteroscedastic T-Process Simulation Metamodeling Approach and Its Application in Inventory Control and Optimization, " the 2017 Winter Simulation Conference, Las Vegas, NV, December 2017.
"A Variational Bayesian Inference-based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling," the IMS/ASA Spring Research Conference, Rutgers–New Brunswick, NJ, May 2017.
"The Effects of Estimation of Heteroscedasticity on Stochastic Kriging," the INFORMS 2016 Annual Meeting, Nashville, TN, November 2016.
"Novel Sequential Experimental Designs for Stochastic Kriging,'' the INFORMS 2015 Annual Meeting, Philadelphia, PA, November 2015.
"Sequential Experimental Designs for Stochastic Kriging," the 2014 Winter Simulation Conference, Savannah, GA, December 2014.
"Sequential Experimental Designs for Stochastic Kriging," the Uncertainty in Computer Models 2014 Conference, Sheffield, UK, July 2014.
"Stochastic Kriging for Estimating Quantile-Based Performance Measures," the 2013 Winter Simulation Conference, Washington D.C., December 2013.
"Stochastic Kriging with Qualitative Factors," the 2013 Winter Simulation Conference, Washington D.C., December 2013.
"Some Recent Developments of Stochastic Kriging," presentation given at the INFORMS 2013 Annual Meeting, Minneapolis, MN, October 2013.
"Determine the Optimal Dosage — A Bayesian Model-Based Approach," presentation given at the INFORMS 2013 Annual Meeting, Minneapolis, MN, October 2013.
"Stochastic Kriging for Conditional Value-at-Risk and Its Sensitivities," presentation given at the 2012 Winter Simulation Conference, Berlin, Germany, December 2012.
"Enhancing Stochastic Kriging Metamodels with Gradient Estimators," presentation given in the Ph.D. Colloquiums and Poster Session at the 2011 Winter Simulation Conference, Phoenix, Arizona, December 2011.
"Stochastic Kriging Metamodels Plus: Exploiting Gradient Estimates," presentation given in the Joint Session I-Sim/QSR: Design of Experiments and Statistical Analysis for Simulation at the INFORMS 2011 Annual Meeting, November 2011.
"Common Random Numbers and Stochastic Kriging," presentation given at the 2010 Winter Simulation Conference, Baltimore, MD, December 2010.
"Common Random Numbers and Stochastic Kriging," presentation given at the 2011 Spring Research Conference on Statistics in Industry and Technology, Evanston, IL, June 2011.

Useful and Interesting Links

Winter Simulation Conference Archive, http://informs-sim.org/
Online lectures on various topics given by world-class scholars, http://videolectures.net/
The Gaussian Processes website, http://www.gaussianprocess.org/
The Gaussian Process Summer School Lectures, https://mlatcl.github.io/gpss/
A wealth of online lecture notes on Mathematics, Economics, and Econometrics, etc., http://econphd.econwiki.com/notes.htm#Mathematics