Henry Lam

Department of Industrial & Operations Engineering

University of Michigan

1205 Beal Ave.

Ann Arbor, MI 48109

Phone: 734-647-8722

Email: khlam (at)

I am currently an Assistant Professor in the Department of Industrial & Operations Engineering at the University of Michigan, Ann Arbor.


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


I received my Ph.D. degree in statistics at Harvard University in 2011.

My CV.

Research Interests

·       Stochastic simulation, Monte Carlo methods

·       Robust and stochastic optimization

·       Risk analysis

·       Statistics and machine learning


University of Michigan, Ann Arbor

·       IOE574: Advanced Simulation Analysis: Winter 2016

·       IOE474: Simulation Analysis: Fall 2015, 2016


Boston University

·       MA570: Stochastic Methods in Operations Research: Spring 2014

·       MA569: Optimization Methods in Operations Research: Fall 2011 | Fall 2012 | Fall 2013

·       MA115: Statistical Methods I: Fall 2014

·       MA116: Statistical Methods II: Spring 2012 | Spring 2013 | Spring 2014

·       MA881: Topics in Applied Probability: Fall 2011


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: Eunshin Byon).

·       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.

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)

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

Research Projects

Robust Stochastic and Simulation Analysis

·       Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation, invited tutorial, to appear in Proceedings of the Winter Simulation Conference (WSC) 2016

·       Approximating data-driven joint chance-constrained programs via uncertainty set construction, with J. Hong and Z. Huang, to appear in Proceedings of the Winter Simulation Conference (WSC) 2016. Finalist, Best Theoretical Paper, Winter Simulation Conference 2016.

·       Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization, under review in Operations Research. Second Prize, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition 2016.

·       Reconstructing input models in stochastic simulation, with A. Goeva, H. Qian and B. Zhang, under revision in Operations Research. Short version appeared in Proceedings of the Winter Simulation Conference (WSC) 2014.

·       The empirical likelihood approach to quantifying uncertainty in sample average approximation, with E. Zhou, under revision in Operations Research Letters. Short version appeared in Proceedings of the Winter Simulation Conference (WSC) 2015.

·       Tail analysis without parametric models: A worst-case perspective, with C. Mottet, under revision in Operations Research.

·       Robust analysis in stochastic simulation: Computation and performance guarantees, with S. Ghosh, under revision in Operations Research.

·       Sensitivity to serial dependency of input processes: A robust approach, accepted in Management Science, 2016.

·       Mirror descent stochastic approximation for computing worst-case stochastic input models, with S. Ghosh, Proceedings of the Winter Simulation Conference (WSC) 2015.

·       A statistical perspective on linear programs with uncertain parameters, with J. Hong, Proceedings of the Winter Simulation Conference (WSC) 2015.

·       Simulating tail events with unspecified tail models, with C. Mottet, Proceedings of the Winter Simulation Conference (WSC) 2015.

·       Robust sensitivity analysis for stochastic systems, Mathematics of Operations Research, 41(4), 1248-1275, 2016. INFORMS Junior Faculty Interest Group (JFIG) Paper Competition 2012, Finalist.

·       Robust rare-event performance analysis with natural non-convex constraints, with J. Blanchet and C. Dolan, Proceedings of the Winter Simulation Conference (WSC) 2014.

·       Iterative methods for robust estimation under bivariate distributional uncertainty, with S. Ghosh, Proceedings of the Winter Simulation Conference (WSC) 2013.


Monte Carlo Methods

·       Uncertainty quantification of stochastic simulation for black-box computer experiments, with Y. Choe and E. Byon, under review in Methodology and Computing in Applied Probability.

·       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.

·       Efficient rare-event simulation for perpetuities, with J. Blanchet and B. Zwart, Stochastic Processes and Their Applications, 122(10), 3361–3392, 2012.

·       State-dependent importance sampling for rare-event simulation: recent advances, with J. Blanchet, Surveys in Operations Research and Management Science, 17(1), 38-59, 2012. Short version appeared in Proceedings of the Winter Simulation Conference (WSC) 2011.

·       Efficient importance sampling under partial information, Proceedings of the Winter Simulation Conference (WSC) 2012.

·       Importance sampling for actuarial cost analysis under a heavy traffic model, with J. Blanchet, Proceedings of the Winter Simulation Conference (WSC) 2011.

·       Rare-event simulation for a slotted time M/G/s model, with J. Blanchet and P. Glynn, Queueing Systems: Theory and Applications, 63, 33-57, 2009.


Applied Probability and Risk Analysis

·       Two-parameter sample path large deviations for infinite server queues, with J. Blanchet and X. Chen, Stochastic Systems, 4(1), 206-249, 2014.

·       A heavy traffic approach to modeling large life insurance portfolios, with J. Blanchet, Insurance Mathematics and Economics, 53(1), 237-251, 2013.

·       Uniform large deviations for heavy-tailed queues under heavy traffic, with J. Blanchet, Bulletin of the Mexican Mathematical Society, Bol. Soc. Mat. Mexicana, 19(3), 2013 Special Issue for the International Year of Statistics.

·       Information dissemination via random walks in d-dimensional space, with Z. Liu, M. Mitzenmacher, X. Sun and Y. Wang, Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA) 2012. Full version.

·       Chernoff-Hoeffding bounds for Markov chains: generalized and simplified, with K. M. Chung, Z. Liu and M. Mitzenmacher, Proceedings of the Symposium on Theoretical Aspects of Computer Science (STACS) 2012. Full version.

·       Corrections to the Central Limit Theorem for heavy-tailed probability densities, with J. Blanchet, M. Z. Bazant and D. Burch, Journal of Theoretical Probability, 24(4), 895-927, 2011.

·       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

·       Accelerated evaluation of automated vehicles using piecewise mixture distribution models, with Z. Huang, D. Zhao, D. LeBlanc and H. Peng, submitted to IEEE International Conference on Robotics and Automation.

·       Accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques, with D. Zhao, H. Peng, S. Bao, D. J. LeBlanc, K. Nobukawa, C. S. Pan, IEEE Transactions on Intelligent Transportation Systems, Articles in advance, PP(99), 1-13, 2016.

·       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.

·       Accelerated evaluation of automated vehicles based on importance sampling, with D. Zhao, H. Peng, S. Bao, K. Nobukawa, D. J. LeBlanc and C. S. Pan, Proceedings of the ASME Dynamic Systems and Control Conference, 2015.

·       Learning about social learning in MOOCs: from statistical analysis to generative model, with C. Brinton, M. Chiang, S. Jain, Z. Liu and F. Wong, IEEE Transactions on Learning Technology, 7(4), 346-359, 2014.

·       From Black-Scholes to online learning: dynamic hedging under adversarial environments, with Z. Liu, submitted.

·       A Bayesian framework for online classifier ensemble, with Q. Bai and S. Sclaroff, International Conference on Machine Learning (ICML), 2014.

·       Why Steiner-tree algorithms work for community detection, with M. Chiang, Z. Liu and V. Poor, International Conference on Artificial Intelligence and Statistics (AISTATS), 2013. Supplementary materials.

·      Statistical platform to discern spatial and temporal coordination of endothelial sprouting, with W. Yuen, N. Du, D. Shvartsman, P. Arany, and D. Mooney, Integrated Biology, 4(3), 292-300. 2012.


Other Teaching Experience

Teaching Fellow in Harvard University, Cambridge, MA:

·       STAT104: Introduction to Quantitative Methods, Fall 2006

·       STAT171: Stochastic Processes, Spring 2007

·       STAT139/239: Linear Models, Fall 2007


Industry Experience

·       Citigroup Global Markets and Banking, Equity Derivatives Trading, Hong Kong, Summer 2009

·       Lehman Brothers, Investment-Linked Insurance Structuring, Hong Kong, Summer 2008

·       Hewitt Associate LLC, Pension and Compensation Statistical Analyst, Hong Kong, Summer 2005

·       Standard Chartered Bank, Corporate Banking, Hong Kong, Summer 2001-2003