Haihao Lu is the Cecil and Ida Green Career Development Assistant Professor, and an Assistant Professor of Operations Research/Statistics at the MIT Sloan School of Management. Before joining MIT Sloan, he was an Assistant Professor at the University of Chicago Booth School of Business and a faculty researcher at Google Research's large-scale optimization team. He obtained his PhD degree in Mathematics and Operations Research at MIT in 2019.

Lu’s research lies at the intersection of optimization, computation, and data science, with a focus on pushing the computational and mathematical frontiers of large-scale optimization. Much of his work is inspired by real-world challenges faced by leading technology companies and optimization software companies. Currently, he is particularly excited about two lines of research:

First-order methods and scalable solvers: Lu develops new first-order optimization algorithms, theoretical guarantees, and computational tools to accelerate and scale mathematical programming using modern computing architectures such as GPUs and distributed systems. These tools address a range of problems, including linear programming, quadratic programming, semidefinite programming, and nonlinear programming. Notably, the PDLP algorithm he codeveloped has been widely adopted by industry-leading solvers and tech companies, including Google, NVIDIA, Gurobi, COPT, HiGHS, Knitro, and FICO Xpress.

Data-driven optimization for resource allocation: Lu also designs new algorithms with provable performance guarantees for allocating scarce resources in uncertain environments. A key application area is budget pacing in online advertising, where his algorithms have been deployed by major platforms such as Google and eBay to improve efficiency and robustness.

His research has been recognized by several research awards, including COIN-OR Cup, the Beale—Orachard-Hays Prize, the INFORMS Optimization Society Young Researchers Prize, the INFORMS Michael H. Rothkopf Junior Research Paper Prize (first place), and the INFORMS Revenue Management and Pricing Section Prize. The algorithms and software developed through his research have had real-world impact, generating substantial revenue and advancing the state of practice in large-scale optimization.