Henry Lam

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.