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-advise with Eric Kolaczyk),
graduated 2017. Now post-doc at Broad Institute of MIT and Harvard.

·Clementine Mottet (BU
Stat). Now data scientist at TripAdvisor.

·AmirhosseinMeisami (UM IOE; co-advise with
Mark van Oyen)

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

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

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

·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

·Efficiently testing automated vehicles under jointly
distributed uncertainty, with Z. Huang, Y. Guo and D.
Zhao, submitted to IEEE Transactions on
Intelligent Vehicles. Short version appeared in IEEE International Conference on Intelligent Transportation Systems
(ITSC): Workshop, 2017.

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

·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 (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.

·Machine teaching via simulation optimization, with
B. Zhang, NIPS Workshop on Machine
Learning from and for Adaptive User Technologies: From Active Learning and
Experimentation to Optimization and Personalization, 2015.