Henry Lam


312 S.W. Mudd

Tel(212) 854-2942
Fax(212) 854-8103

Henry Lam researches Monte Carlo simulation and optimization under uncertainty. His work focuses on the quantification and mitigation of statistical errors and risks in integrating these methodologies with data, and the design and analysis of efficient computation and solution machineries. Henry is also broadly interested in the intersecting domains of computational probability, stochastic and robust optimization, and machine learning.

Research Interests

Monte Carlo simulation, optimization under uncertainty, rare event analysis, statistics and machine learning.

Lam builds methods for computational simulation and optimization used in data-driven decision-making. The complexities in modern industrial and business applications make it challenging to devise these methods using conventional statistical tools, due to the high-dimensionality of uncertain input or model space, heavy computation burden in high-fidelity simulation models, and structural complexities in data utilization in decision-making. Lam develops techniques that are both statistically and computationally efficient to address these challenges. These techniques are used, for example, to assess the propagation and aggregation of uncertainties from input and model calibration in large-scale stochastic simulation, to validate and enhance the performances of solutions in uncertain optimization, and to manage risks stemming from extreme events. His application areas include autonomous vehicle testing, financial risk analytics and online marketing.

Lam received his PhD degree in statistics from Harvard University in 2011 and was on the faculty of Boston University and the University of Michigan before joining Columbia in 2017. He is a recipient of the NSF CAREER Award in 2017 and serves on the editorial boards of Operations Research and INFORMS Journal on Computing.

Professional Experience

  • Associate Professor, Industrial Engineering and Operations Research, Columbia University, 2017-
  • Assistant Professor, Industrial and Operations Engineering, University of Michigan, Ann Arbor, 2015–2017
  • Assistant Professor, Mathematics and Statistics, Boston University, 2011–2014

Professional Affiliations

  • Associate Editor, Operations Research, 2015-
  • Associate Editor, INFORMS Journal on Computing, 2016-
  • INFORMS Applied Probability Society Council Member, 2017-

Honors & Awards

  • NSF Faculty Early Career Development (CAREER) Award, 2017
  • INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, Second Prize, 2016
  • Adobe Digital Marketing Research Award, 2016
  • Finalist, Best Theoretical Paper, Winter Simulation Conference, 2016
  • INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, Finalist, 2012
  • INFORMS George Nicholson Student Paper Competition Honorable Mention Prize, 2010

Selected Publications

  • Lam, H., Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization, accepted in Operations Research, 2018.
  • Ghosh, S. and Lam, H., Robust analysis in stochastic simulation: Computation and performance guarantees, accepted in Operations Research, 2018.
  • Lam, H., Sensitivity to serial dependency of input processes: A robust approach, Management Science, 64(3), 1311-1327, 2018.
  • Lam, H., and Mottet, C., Tail analysis without parametric models: A worst-case perspective, Operations Research, 65(6), 1696-1711, 2017.
  • Zhao, D., Lam, H., Peng, H., Bao, S., LeBlanc, D. J., Nobukawa, K. and Pan, C. S., Accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques, IEEE Transactions on Intelligent Transportation Systems, 18(3), 595-607, 2017.
  • Lam, H., Robust sensitivity analysis for stochastic systems, Mathematics of Operations Research, 41(4), 1248-1275, 2016.