I am interested in building
robust and statistically principled methodologies for Monte Carlo simulation,
risk analysis, and stochastic and simulation-based optimization.

Support from the following funding sources are gratefully acknowledged:

·National Security Agency (NSA) Young Investigator
Grant H98230-13-1-0301. Title: “Design of Robust Methodologies for Efficient
Simulation and Sensitivity Analysis for Stochastic Systems”. Duration:
September 2013-September 2014. Role: PI.

·National Science Foundation (NSF)
CMMI-1400391/1542020. Title: “A Sensitivity Approach to Assessing Model
Uncertainty for Stochastic Systems”. Duration: July 2014-June 2017. Role: PI.

·National Science Foundation (NSF)
CMMI-1436247/1523453. Title: “Collaborative Research: Modeling and Analyzing
Extreme Risks in Insurance and Finance”. Duration: September 2014-August 2017.
Role: PI (lead-PI: Jose Blanchet, PI: Qihe Tang).

·MCubed. Title: “Data-driven
Methods in Simulation Modeling and Optimization for Large-scale Dynamic
Systems”. Duration: November 2015-October 2017. Role: co-PI (PI: Hyun-Soo Ahn, co-PI: EunshinByon).

·UM Mobility Transformation Center (MTC). Title:
“Development of Evaluation Approaches and the Certificate System for Automated
Vehicles Based on the Accelerated Evaluation”. Duration: May 2016-December
2017. Role: PI (co-PI: David LeBlanc).

·Adobe Digital Marketing Research Award 2016. Title:
“Scalable Dynamic Optimization in Online Marketing Campaigns”. Role: PI.

·National Science Foundation (NSF) CMMI-1653339.
Title: “CAREER: Optimization-based Quantification of Statistical Uncertainty in
Stochastic and Simulation Analysis”. Duration: May 2017-April 2022. Role: PI.

Editorial Appointments

·Associate Editor, Operations Research, 2015-

·Associate Editor, INFORMS Journal on Computing, 2016-

Ph.D. Students

·Alexandrina Goeva (BU
Stat; co-advised with Eric Kolaczyk), graduated 2017.
First position: Post-doc, Broad Institute of MIT and Harvard.

·Clementine Mottet (BU
Stat), graduated 2017. First position: Data scientist, TripAdvisor.

·AmirhosseinMeisami (UM IOE; co-advised with Mark van Oyen), graduated
2018. First position: Data scientist, Adobe.

·Zhiyuan Huang (UM IOE)

·Huajie Qian (Columbia
IEOR)

·Xinyu Zhang
(Columbia IEOR)

Research Projects

Simulation Uncertainty Quantification
and Robust Analysis

·Maximum likelihood estimation by Monte Carlo
simulation: Towards data-driven stochastic modeling, with Y. Peng, M. Fu and B.
Heidergott, under
review in Operations Research.

·Constructing simulation output intervals under input
uncertainty via data sectioning, with P. W. Glynn, accepted in the Winter Simulation Conference (WSC), 2018.

·Subsampling variance for input uncertainty
quantification, with H. Qian, accepted in
the Winter Simulation Conference (WSC), 2018.

·Revisiting direct bootstrap resampling for input
model uncertainty, with R. Barton and E. Song, accepted in the Winter Simulation Conference (WSC), 2018.

·Uncertainty quantification on simulation analysis
driven by random forests, with A. Meisami and M. Van
Oyen, Proceedings of the Winter
Simulation Conference (WSC) 2017.

·Improving prediction from stochastic simulation via
model discrepancy learning, with M. Plumlee and X.
Zhang, Proceedings of the Winter
Simulation Conference (WSC) 2017.

·On efficiencies of stochastic optimization procedures
under importance sampling, with G. Jiang and M. Fu, accepted in the Winter Simulation Conference (WSC), 2018.

·Sampling uncertain constraints under parametric
distributions, with F. Li, accepted in
the Winter Simulation Conference (WSC), 2018.

·Assessing solution quality in stochastic
optimization via bootstrap aggregating, with H. Qian, accepted in the Winter Simulation Conference (WSC), 2018.

·Computing worst-case expectations given marginals via simulation, with J. Blanchet and F. He, Proceedings of the Winter Simulation Conference
(WSC) 2017.

·Designing importance samplers to simulate machine
learning predictors via optimization, with Z. Huang and D. Zhao, accepted in the Winter Simulation Conference
(WSC), 2018.

·Rare-event simulation without structural
information: A learning-based approach, with Z. Huang and D. Zhao, accepted in the Winter Simulation Conference
(WSC), 2018.

·Variance reduction method for extreme quantile
estimation, with Q. Pan and E. Byon, accepted in the Institute of Industrial and
Systems Engineers (IISE) Annual conference, 2018.

·Rare-event simulation for
many-server queues, with J. Blanchet, Mathematics of Operations Research, 39(4), 1142-1178, 2014. Honorable Mention Prize, INFORMS George Nicholson Paper
Competition 2010.

·Exact asymptotics for
infinite-server queues. Preliminary version appeared in Proceedings of the 6th International Conference on Queueing Theory and
Network Applications 2011.

Statistical Learning and Applications

·Evaluating autonomous vehicles by integrating
multi-fidelity tests, with Z. Huang, M. Arief and D.
Zhao, under review in the IEEE
International Conference on Intelligent Transportation Systems (ITSC).

·Efficiently testing automated vehicles under jointly
distributed uncertainty, with Z. Huang, Y. Guo and D.
Zhao, under review in IEEE Transactions
on Intelligent Vehicles.

·Sequential learning under probabilistic constraints,
with A. Meisami, C. Dong and A. Pani,
accepted in the Conference on Uncertainty
in Artificial Intelligence (UAI), 2018.

·Achieving optimal bias-variance tradeoff in online
derivative estimation, with T. Duplay and X. Zhang, accepted in the Winter Simulation Conference
(WSC), 2018.

·A versatile approach for the evaluation and testing
of automated vehicles based on kernel methods, with Z. Huang, Y. Guo and D. Zhao, accepted
in the American Control Conference (ACC) 2018.

·An accelerated testing approach for automated
vehicles with background traffic described by joint distributions, with Z.
Huang and D. Zhao, IEEE International
Conference on Intelligent Transportation Systems (ITSC), 2017.

·Towards affordable on-track testing for autonomous
vehicle – A kriging-based statistical approach, with Z. Huang and D. Zhao, IEEE International Conference on Intelligent
Transportation Systems (ITSC) 2017.

·Sequential experimentation to efficiently test
automated vehicles, with Z. Huang and D. Zhao, Proceedings of the Winter Simulation Conference (WSC), 2017.

·Evaluation of automated vehicles in the frontal
cut-in scenario - An enhanced approach using piecewise mixture model, with Z.
Huang, D. Zhao, D. J. LeBlanc and H. Peng, Proceedings
of the IEEE International Conference on Robotics and Automation (ICRA) 2017.