Title: Efficient Uncertainty Quantification in Simulation Analysis

Abstract: Simulation-based prediction, for instance in discrete-event analysis and machine learning, relies on models that often are contaminated with errors when calibrating from data. These errors, if overlooked, can result in incorrect inference and underestimation of risks that degrade decision-making. Existing approaches to quantify these errors face several challenges from high computational demand, undercoverage, to the opaqueness in parameter tuning. We present several methods to combat these issues, by injecting subsampling, distributionally robust optimization, and random perturbation respectively into simulation runs. We explain the statistical mechanisms of these approaches and why they help resolve each of the discussed challenges.

Bio: Huajie Qian is a Ph.D. candidate in the department of Industrial Engineering and Operations Research at Columbia University, advised by Henry Lam. His research borrows tools from statistics and machine learning to develop data-driven methodologies for stochastic simulation and optimization that can deal with uncertainties from data in an efficient and principled way. He received his M.S. degree in Applied and Interdisciplinary Mathematics from University of Michigan, and B.S. degree in Mathematics from Fudan University.