Haihao (Sean) Lu

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Haihao (Sean) Lu

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Haihao (Sean) Lu is an Assistant Professor of Operations Research/Statistics at the MIT Sloan School of Management. Before joining 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 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 and 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.

Publications

"On the Linear Convergence of Extra-gradient Methods for Nonconvex-nonconcave Minimax Problems."

Hajizadeh, Saeed, Haihao Lu, and Benjamin Grimmer. INFORMS Journal on Optimization. Forthcoming. arXiv Preprint.

"A J-symmetric Quasi-newton Method for Minimax Problems."

Asl, Azam, Haihao Lu, and Jinwen Yang. Mathematical Programming Vol. 204, No. 1-2 (2024): 207-254.

"Infeasibility Detection with Primal-dual Hybrid Gradient for Large-scale Linear Programming."

Applegate, David, Mateo Díaz, Haihao Lu, and Miles Lubin. SIAM Journal on Optimization Vol. 34, No. 1 (2024).

"Online Ad Procurement in Non-stationary Autobidding Worlds."

Jason Cheuk Nam Liang, Haihao Lu, and Baoyu Zhou. In Proceedings of the 37th Conference on Neural Information Processing Systems, New Orleans, LA: December 2023.

"Faster First-order Primal-dual Methods for Linear Programming Using Restarts and Sharpness."

Applegate, David, Oliver Hinder, Haihao Lu, and Miles Lubin. Mathematical Programming Vol. 201, No. 1-2 (2023): 133-184.

"The Landscape of the Proximal Point Method for Nonconvex–nonconcave Minimax Optimization."

Grimmer, Benjamin, Haihao Lu, Pratik Worah, and Vahab Mirrokni. Mathematical Programming Vol. 201, No. 1-2 (2023): 373-407.

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