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 Math
& Stat)

·Clementine Mottet (BU Math
& Stat)

·Huajie Qian (UM
Applied & Interdisciplinary Math; co-advise with Virginia Young)

·Zhiyuan Huang (UM IOE)

·Xinyu Zhang (UM IOE)

·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 of stochastic simulation
for black-box computer experiments, with Y. Choe and
E. Byon, accepted
(up to minor revision) in Methodology and Computing in Applied Probability.Selected for the Natrella
Invited Section in the American Statistical Association (ASA) Quality &
Productivity Research Conference 2015.

·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

·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.

·Accelerated
evaluation of automated vehicles using piecewise mixture models, with Z.Huang, D. Zhao and D. J. LeBlanc, under
revision in IEEE Transactions on Intelligent Transportation Systems. Short
version to appear in Proceedings of the
IEEE International Conference on Robotics and Automation 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.