Industrial Engineering and Operations Research
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.
- 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
- Associate Editor, Operations Research, 2015-
- Associate Editor, INFORMS Journal on Computing, 2016-
- INFORMS Applied Probability Society Council Member, 2017-
- 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
- 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.