I am an assistant professor of Operations Management at the University of Chicago Booth School of Business. My research interests are in 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. In particular, I am interested in (i) theory of convex and non-convex optimization motivated by statistical/machine learning problems; (ii) data-driven decision making with applications in advertisement allocation and machine scheduling; (iii) huge-scaling linear programming solving in the distributed setting (with applications at Google). I am the winner of 2021 INFORMS Optimization Society Young Researchers Prize (see the citation here).
Before joining Booth, I was a visiting researcher at Google Research large-scale optimization team, where I primarily worked on designing and implementing a huge-scale linear programming solver. I obtained my Ph.D degree dual in Operations Research and Applied Mathematics at MIT in 2019, working with Robert M. Freund. Prior to MIT, I obtained my B.S. in Mathematics at Shanghai Jiao Tong University in 2014.
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems, with Santiago Balseiro and Vahab Mirrokni, to appear in Operations Research. [arXiv] [slides]
- The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization, Benjamin Grimmer, Haihao Lu, Pratik Worah and Vahab Mirrokni. [arXiv] [slides]
- An O(s^r)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems, Haihao Lu, to appear in Mathematical Programming. [arXiv] [slides]
Relatively-Smooth Convex Optimization by First-Order Methods, and Applications, Haihao Lu, Robert M. Freund and Yurii Nesterov, SIAM Journal on Optimization 28(1), 333–354, 2018. [arXiv]
See my research page for a full list of publications.
Address: 5807 South Woodlawn Avenue
Chicago, IL 60637