Haichen Hu

Haichen Hu is a graduate student in computational science and engineering and civil and environmental engineering whose work bridges statistical learning, reinforcement learning, and optimization for decision-making under uncertainty in operations research. In reinforcement learning, Haichen developed one of the first optimal algorithms with low-frequency oracle calls for policy optimization and offline estimation, substantially reducing computational overhead. More broadly, he unifies sequential decision-making problems—including bandits, model calibration, and active learning—through functional analysis and operator theory, establishing how eigendecay of compact operators governs regret rates. He also established tight excess-risk bounds for black-box predictors in data- and compute-efficient settings, and most recently developed a black-box reduction from nonconvex to online convex optimization for efficient large language model training. As a MathWorks Fellow, Haichen will leverage MATLAB’s Optimization Toolbox and Statistics and Machine Learning Toolbox to solve combinatorial optimization problems in real operations research applications. This work will extend these foundations to sequential prediction and multi-step interaction settings, establishing a basis for black-box-assisted decision-making across operational domains.