http://www.columbia.edu/~khl2114/files/pic_updated.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 received my Ph.D. degree in statistics at Harvard University in 2011.

 

My CV.

Research Interests

I am interested in efficient methodologies and statistical uncertainty quantification for Monte Carlo computation, predictive modeling and data-driven optimization. My current interests include:

·         Monte Carlo methods

·         Uncertainty quantification, calibration, data assimilation for simulation and optimization models 

·         Data-driven robust and stochastic optimization

·         Extremal and rare-event estimation

·         Machine learning, particularly risk evaluation of intelligent systems

Funding

Supports 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: Sep 2013-Jun 2014. Role: PI.

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

·         National Science Foundation (NSF) CMMI-1436247/1523453. Title: “Collaborative Research: Modeling and Analyzing Extreme Risks in Insurance and Finance”. Duration: Sep 2014-Aug 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: Nov 2015-Oct 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-Dec 2017. Role: PI (co-PI: David LeBlanc).

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

·         National Science Foundation (NSF) CMMI-1653339/1834710. Title: “CAREER: Optimization-based Quantification of Statistical Uncertainty in Stochastic and Simulation Analysis”. Duration: May 2017-Apr 2022. Role: PI.

·         National Science Foundation (NSF) IIS-1849280. Title: “Collaborative Research: Unsupervised Rare Event Learning – With Applications on Autonomous Vehicles”. Duration: Feb 2019-Jan 2022. Role: PI (lead-PI: Ding Zhao).

·         Google and Tides Foundation. Title: “EMS Resource Deployment Modeling” (with New York City Fire Department). Duration: Jan 2020-Jan 2022. Role: co-PI (PI: Andrew Smyth).

·         JPMorgan Chase Faculty Research Award 2020. Title: “Calibrating Large-Scale Simulation Models via Distributionally Robust Optimization”. Duration: May 2020-Aug 2021. Role: PI.

·         Columbia Urban Technology Pilot Award. Title: “Optimizing Emergency Response during a Pandemic in Urban Environments”. Duration: Sep 2020-Sep 2021. Role: co-PI (PI: Andrew Smyth, co-PI: Jay Sethuraman).

Editorial Appointments

·         Associate Editor, Operations Research, 2015-

·         Associate Editor, INFORMS Journal on Computing, 2016-

·         Editorial Board, Stochastic Models, 2019-

·         Editorial Board, Journal of Applied Probability / Advances in Applied Probability, 2020-

·         Associate Editor, Manufacturing and Service Operations Management, 2021-

·         Associate Editor, Operations Research Letters, 2021-

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: TripAdvisor.

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

·         Zhiyuan Huang (UM IOE), graduated 2020. First position: Post-doc, Carnegie Mellon University.

·         Huajie Qian (Columbia IEOR), graduated 2020. First position: Alibaba.

·         Kumar Goutam (Columbia IEOR; co-advised with Vineet Goyal), graduated 2020. First position: Amazon.

·         Fengpei Li (Columbia IEOR; co-advised with Jose Blanchet), graduated 2020. First position: Morgan Stanley.

·         Xinyu Zhang (Columbia IEOR)

·         Yuanlu Bai (Columbia IEOR)

·         Haofeng Zhang (Columbia IEOR)

·         Yibo Zeng (Columbia IEOR)

·         Zhenyuan Liu (Columbia IEOR)

·         Shengyi He (Columbia IEOR)

Publications

Simulation Uncertainty Quantification and Model Calibration

·         Calibrating over-parametrized simulation models: A framework via eligibility set, with Y. Bai, T. Balch, H. Chen, D. Dervovic and S. Vyetrenko.

·         Model calibration via distributionally robust optimization: On the NASA Langley Uncertainty Quantification Challenge, with Y. Bai and Z. Huang, Mechanical Systems and Signal Processing, Special Issue on the NASA Challenge, 164, 1-19, 2021.

Short version: A distributionally robust optimization approach to the NASA Langley Uncertainty Quantification Challenge, ESREL-PSAM 2020

·         Subsampling to enhance efficiency in input uncertainty quantification, with H. Qian, forthcoming in Operations Research, 2021.

Short version: Subsampling variance for input uncertainty quantification, WSC 2018

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

Short version: The empirical likelihood approach to simulation input uncertainty, WSC 2016

·         A shrinkage approach to improve direct bootstrap resampling under input uncertainty, with E. Song and R. Barton.

Short version: Revisiting direct bootstrap resampling for input model uncertainty, WSC 2018

·         Maximum likelihood estimation by Monte Carlo simulation: Towards data-driven stochastic modeling, with Y. Peng, M. Fu and B. Heidergott, Operations Research, 68(6), 1896-1912, 2020.

·         Calibrating input parameters via eligibility sets, with Y. Bai, Winter Simulation Conference (WSC) 2020.

·         Optimization-based calibration of simulation input models, with A. Goeva, H. Qian and B. Zhang, Operations Research, 67(5), 1362-1382, 2019.

Preliminary version: Reconstructing input models via simulation optimization, WSC 2014

·         Robust analysis in stochastic simulation: Computation and performance guarantees, with S. Ghosh, Operations Research, 67(1), 232-249, 2019.

(Also circulated under the title “Computing worst-case input models in stochastic simulation”)

·         An uncertainty quantification method for inexact simulation models, with M. Plumlee.

Short version: Learning stochastic model discrepancy, WSC 2016

·         Random perturbation and bagging to quantify input uncertainty, with H. Qian, Winter Simulation Conference (WSC) 2019.

·         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, 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 ASA Quality & Productivity Research Conference 2015

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

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

·         Robust sensitivity analysis for stochastic systems, Mathematics of Operations Research, 41(4), 1248-1275, 2016.

Finalist, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition 2012

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

·         Mirror descent stochastic approximation for computing worst-case stochastic input models, Winter Simulation Conference (WSC) 2015.

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

 

Data-Driven Optimization

·         Higher-order expansion and Bartlett correctability of distributionally robust optimization, with S. He.

·         Complexity-free generalization via distributionally robust optimization, with Y. Zeng.

·         On the impossibility of statistically improving empirical optimization: A second-order stochastic dominance perspective.

·         General feasibility bounds for sample average approximation via Vapnik-Chervonenkis dimension, with F. Li, under review in SIAM Journal on Optimization.

·         Combating conservativeness in data-driven optimization under uncertainty: A solution path approach, with H. Qian, under revision in Management Science.

Short version: Validating optimization with uncertain constraints, WSC 2019

·         Bounding optimality gap in stochastic optimization via bagging: Statistical efficiency and stability, with H. Qian.

Short version: Assessing solution quality in stochastic optimization via bootstrap aggregating, WSC 2018

·         Efficient learning for clustering and optimizing context-dependent designs, with H. Li and Y. Peng, under revision in Operations Research.

Short version: Context-dependent ranking and selection under a Bayesian framework, WSC 2020

Finalist, Best Theoretical Paper, Winter Simulation Conference 2020

·         Assortment optimization over dense universe is easy, with K. Goutam and V. Goyal.

·         Enhanced balancing of bias-variance tradeoff in stochastic estimation: A minimax perspective, with X. Zhang and X. Zhang, under minor revision in Operations Research.

·         Distributionally constrained black-box stochastic gradient estimation and optimization, with J. Zhang, under review in Operations Research.

Short version: Distributionally constrained stochastic gradient estimators using noisy function evaluations, WSC 2020

·         Minimax efficient finite-difference stochastic gradient estimators using black-box function evaluations, with H. Li and X. Zhang, Operations Research Letters, 49(1), 40-47, 2021.

Short version: Minimax efficient finite-difference gradient estimation, WSC 2019

Spotlight paper in Operations Research Letters

·         Learning-based robust optimization: Procedures and statistical guarantees, with L. J. Hong and Z. Huang, Management Science, 67(6), 3447-3467, 2021. Codes available here.

Short version: Approximating data-driven joint chance-constrained programs via uncertainty set construction, WSC 2016

Finalist, Best Theoretical Paper, Winter Simulation Conference 2016

·         Parametric scenario optimization under limited data: A distributionally robust optimization view, with F. Li, ACM Transactions on Modeling and Computer Simulation, 30(4), 21:1-41, 2020.

Short version: Sampling uncertain constraints under parametric distributions, WSC 2018

Best Theoretical Paper, Winter Simulation Conference 2018

·         Optimally tuning finite-difference estimators, with H. Li, Winter Simulation Conference (WSC) 2020.

·         Sample average approximation with functional decisions under shape constraints, with D. Singham, Winter Simulation Conference (WSC) 2020.

·         Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization, Operations Research, 67(4), 1090-1105, 2019. Codes available here.

Second Prize, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition 2016

·         Achieving optimal bias-variance tradeoff in online derivative estimation, with T. Duplay and X. Zhang, 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.

Short version: Quantifying uncertainty in sample average approximation, WSC 2015

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

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

 

Rare-Event and Extremal Risk Estimation

·         Deep Probabilistic Accelerated Evaluation: A robust certifiable rare-event simulation methodology for black-box safety-critical systems, with M. Arief, Z. Huang, G. K. S., Kumar, Y. Bai, S. He, W. Ding, and D. Zhao, International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

·         Convolution bounds on quantile aggregation, with J. Blanchet, Y. Liu and R. Wang.

·         Adaptive importance sampling for efficient stochastic root finding and quantile estimation, with S. He, G. Jiang and M. C. Fu, under review in Operations Research.

Short version: On efficiencies of stochastic optimization procedures under importance sampling, WSC 2018

·         Rare-event simulation for neural network and random forest predictors, with Y. Bai, Z. Huang and D. Zhao, under revision in ACM Transactions on Modeling and Computer Simulation.

Short version: Designing importance samplers to simulate machine learning predictors via optimization, WSC 2018

·         On optimization over tail distributions, with C. Mottet. R package available here.

·         Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models, with Q. Pan, Y. M. Ko and E. Byon, Naval Research Logistics, 67(7), 524-547, 2020.

Short version: Variance reduction method for extreme quantile estimation, IISE Annual conference 2018

·         On the error of naive Monte Carlo rare-event estimators, with Y. Bai, Winter Simulation Conference (WSC) 2020.

·         Robust actuarial risk analysis, with J. Blanchet, Q. Tang and Z. Yuan, North American Actuarial Journal, 23(1), 33-63, 2019.

·         On the impacts of tail model uncertainty in rare-event estimation, with Z. Huang, Winter Simulation Conference (WSC) 2019.

·         Rare-event simulation without structural information: A learning-based approach, with Z. Huang and D. Zhao, Winter Simulation Conference (WSC) 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, Winter Simulation Conference (WSC) 2015.

·         Robust rare-event performance analysis with natural non-convex constraints, with J. Blanchet and C. Dolan, 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

·         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), Special Issue for the International Year of Statistics, 19(3), 2013.

·         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: Rare-event simulation techniques, Invited Tutorial, WSC 2011.

·         Information dissemination via random walks in d-dimensional space, with Z. Liu, M. Mitzenmacher, X. Sun and Y. Wang, 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, Symposium on Theoretical Aspects of Computer Science (STACS) 2012. Full version.

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

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

·         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 in International Conference on Queueing Theory and Network Applications 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.

 

Statistical Learning and Decision Analytics

·         Learning prediction intervals for regression: Generalization and calibration, with H. Chen, Z. Huang, H. Qian and H. Zhang, International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

·         Efficient uncertainty quantification and exploration for reinforcement learning, with Y. Zhu and J. Dong, under revision in Operations Research.

·         Quantile regression forests for individualized surgery scheduling, with A. Meisami, M. Van Oyen, C. Stromblad and N. Kastango, under revision in Health Care Management Science.

·         A new likelihood ratio method for training artificial neural networks, with Y. Peng, L. Xiao, B. Heidergott and J. Hong, forthcoming in INFORMS Journal on Computing, 2021.

·         Robust importance weighting for covariate shift, with F. Li and S. Prusty, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

·         Constrained reinforcement learning via policy splitting, with H. Chen, F. Li and A. Meisami, Asian Conference on Machine Learning (ACML), PMLR, 2020.

·         Dynamic congestion pricing for ridesourcing traffic: A simulation optimization approach, with Q. Luo and Z. Huang, Winter Simulation Conference (WSC) 2019.

WSC PhD Colloquium INFORMS I-SIM Award 2019

·         From data to stochastic modeling and decision-making: What can we do better?, with J. Berkhout, B. Heidergott and Y. Peng, Asia-Pacific Journal of Operations Research, Special issue on Simulation Analytics, 36(6), 2019.

·         On the stability of kernelized control functionals on partial and biased stochastic inputs, with H. Zhang, Winter Simulation Conference (WSC) 2019.

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

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

·         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.

 

Simulation and Learning-Based Methods for Autonomous Vehicle Safety Evaluation

·         Evaluation uncertainty in data-driven self-driving testing, with Z. Huang, M. Arief and D. Zhao, IEEE Intelligent Transportation Systems Conference (ITSC), 2019.

·         Accelerated evaluation of automated vehicles using piecewise mixture models, with Z. Huang, D. Zhao and D. J. LeBlanc, IEEE Transactions on Intelligent Transportation Systems, 19(9), 2845-2855, 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.

·         Synthesis of different autonomous vehicles test approaches, with Z. Huang, M. Arief and D. Zhao, IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018.

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

Short version: Accelerated evaluation of automated vehicles in lane change scenario, ASME Dynamic Systems and Control Conference, 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, 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, IEEE International Conference on Robotics and Automation (ICRA) 2017.

Teaching

Columbia University

·         IEOR6711: Stochastic Modeling I: Fall 2019, 2020

·         IEOR4102: Stochastic Modeling for Management Science and Engineering: Spring 2019

·         IEOR8100: Statistical Methods for Simulation and Optimization under Uncertainty: Spring 2019

·         IEOR3404: Simulation Modeling and Analysis: Spring 2018

·         IEOR4100/4101: Probability, Statistics and Simulation: Fall 2017, 2018, 2019, 2020

 

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, 2012, 2013

·         MA115: Statistical Methods I: Fall 2014

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

·         MA881: Topics in Applied Probability: Fall 2011

 

Teaching Fellow in Harvard University

·         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