Haihao (Sean) Lu is an assistant professor of Operations Management at the University of Chicago Booth School of Business. Before joining Chicago Booth, he was a faculty researcher at Google Research's large-scale optimization team. He obtained my Ph.D degree in Mathematics and Operations Research at MIT in 2019.
Lu’s research primarily focuses on extending the computational and mathematical boundaries of methods for solving the large-scale optimization problems that arise in data science, machine learning, and operations research. Most of his research is motivated by real-world applications faced by leading Internet companies. Currently, he is particularly enthused about two lines of research:
- Develop new first-order optimization algorithms and computational tools to scale up large-scale constrained continuous optimization problems by a factor of 1000 compared to the state-of-the-art commercial solvers. These optimization problems include but are not limited to linear programming, quadratic programming, second-order cone programming, and nonlinear programming.
- Develop new data-driven optimization algorithms for the allocation of scarce resources. A motivation for this line of research is the budget pacing in online advertising platforms, where he proposes efficient and robust algorithms that can be applied to real-world applications and studies their provable performance guarantees.
His research has been recognized by several research awards, including INFORMS Optimization Society Young Researchers Prize, INFORMS Michael H. Rothkopf Junior Research Paper Prize (first place), INFORMS Revenue Management and Pricing Section Prize. Notably, the algorithms and software developed in his research have been utilized in leading technology companies and generated significant revenue impacts.
The Landscape of Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization, Benjamin Grimmer, Haihao Lu, Pratik Worah and Vahab Mirrokni.
An O(s^r)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems, Haihao Lu.
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems, with Santiago Balseiro and Vahab Mirrokni.
Relatively-Smooth Convex Optimization by First-Order Methods, and Applications, Haihao Lu, Robert M. Freund and Yurii Nesterov
2023 - 2024 Course Schedule
|32100||Data Analysis with R and Python||2024 (Winter)|