http://umich.edu/~khlam/files/khlam.jpg

Henry Lam

Department of Industrial Engineering & Operations Research

Columbia University

500 W. 120th Street

New York, NY 10027

Email: henry.lam (at) columbia.edu

I am an Associate Professor in the Department of Industrial Engineering & Operations Research at Columbia University.

 

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

·       Monte Carlo simulation

·       Uncertainty quantification

·       Robust and stochastic optimization

·       Extreme risk analysis

·       Statistics and machine learning

Teaching

Columbia University

·       IEOR3404: Simulation: Spring 2018

·       IEOR4100/4101: Probability Models for Management Science and Engineering: Fall 2017

 

University of Michigan, Ann Arbor

·       IOE574: Advanced Simulation Analysis: Winter 2016, 2017

·       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

Funding

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.

·       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.

·       Amirhossein Meisami (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

·       Optimization-based quantification of simulation input uncertainty via empirical likelihood, with H. Qian, under revision in Management Science.

·       An uncertainty quantification method for inexact simulation models, with M. Plumlee, under revision in Operations Research.

·       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.

·       Optimization-based calibration of simulation input models, with A. Goeva, H. Qian and B. Zhang, accepted in Operations Research, 2018.

·       Robust analysis in stochastic simulation: Computation and performance guarantees, with S. Ghosh, accepted in Operations Research, 2018.

·       Sensitivity to serial dependency of input processes: A robust approach, Management Science, 64(3), 1311-1327, 2018.

·       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 of stochastic simulation for black-box computer experiments, with Y. Choe and E. Byon, Methodology and Computing in Applied Probability, 1-18, 2017. 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.

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

·       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.

·       Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation, Invited Tutorial, Proceedings of the Winter Simulation Conference (WSC) 2016.

·       The empirical likelihood approach to simulation input uncertainty, with H. Qian, Proceedings of the Winter Simulation Conference (WSC), 2016.

·       Learning stochastic model discrepancy, with M. Plumlee, Proceedings of the Winter Simulation Conference (WSC), 2016.

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

·       Reconstructing input models via simulation optimization, with A. Goeva and B. Zhang, 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.

 

Optimization under Uncertainty

·       Learning-based robust optimization: Procedures and statistical guarantees, with J. Hong and Z. Huang, under revision in Management Science.

·       Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization, accepted in Operations Research, 2018. Second Prize, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition 2016.

·       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.

·       The empirical likelihood approach to quantifying uncertainty in sample average approximation, with E. Zhou, Operations Research Letters, 45(4), 301-307, 2017.

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

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

·       Quantifying uncertainty in sample average approximation, with E. Zhou, 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.

 

Rare-Event and Extremal Estimation

·       On optimization over tail distributions, with C. Mottet, under revision in INFORMS Journal on Computing. R package available here.

·       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.

·       Tail analysis without parametric models: A worst-case perspective, with C. Mottet, Operations Research, 65(6), 1696-1711, 2017. R package available here.

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

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

·       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.

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

·       Rare-event simulation techniques, with J. Blanchet, Invited Tutorial, Proceedings of the Winter Simulation Conference (WSC) 2011.

·       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

·       Robust actuarial risk analysis, with J. Blanchet, Q. Tang and Z. Yuan, under revision in North American Actuarial Journal.

·       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

·       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.

·       Robust and parallel Bayesian model selection, with M. Zhang and L. Lin, Journal of Computational Statistics and Data Analysis, 127, 229-247, 2018.

·       Accelerated evaluation of automated vehicles using piecewise mixture models, with Z. Huang, D. Zhao and D. J. LeBlanc, IEEE Transactions on Intelligent Transportation Systems, Articles in advance, PP(99), 1-11, 2017.

·       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.

·       Accelerated evaluation of automated vehicles in car-following maneuvers, with D. Zhao, X. Huang, H. Peng, and D. J. LeBlanc, IEEE Transactions on Intelligent Transportation Systems, 19(3), 733-744, 2018.

·       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 and C. S. Pan, IEEE Transactions on Intelligent Transportation Systems, 18(3), 595-607, 2017. UMTRI Transportation Safety Research Symposium Best Poster Award Second Place 2015.

·       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.

·       Accelerated evaluation of automated vehicles in lane change scenario, 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.

·       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.

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

 

Experience

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