Haihao Lu

About

Haihao Lu is the Cecil and Ida Green Career Development Assistant Professor and an Assistant Professor of Operations Research and Statistics at the MIT Sloan School of Management. His 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. 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. His contributions include the development of algorithms like the PDLP algorithm, which has been widely adopted by industry-leading solvers and tech companies including Google, NVIDIA, and Gurobi. Additionally, he designs algorithms with provable performance guarantees for resource allocation in uncertain environments, with applications such as online advertising budget pacing, where his algorithms have been deployed by major platforms like Google and eBay. Lu's research has been recognized with several awards, including the 2026 Sloan Research Fellowship, the 2024 COIN-OR Cup, the 2024 Beale—Orchard-Hays Prize, and the INFORMS prizes, highlighting his significant impact and leadership in the field of optimization and operations research.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematical optimization
• Mathematics
• Machine Learning
• Algorithm
• Engineering
• Applied mathematics
• Geometry

Selected publications

• Randomized Gradient Boosting Machine

  SIAM Journal on Optimization · 2020 · 38 citations

  1st authorCorresponding

  Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 29 October 2018Accepted: 09 June 2020Published online: 07 October 2020Keywordsgradient boosting, ensemble methods, convex optimization, coordinate descent, computational guarantees, first order methodsAMS Subject Headings90C25, 68U01Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8

  DOI

• Regularized Online Allocation Problems: Fairness and Beyond

  Manufacturing & Service Operations Management · 2025-02-20 · 12 citations

  article

  Problem definition: Online allocation problems with resource constraints have a rich history in operations management. In this paper, we introduce the regularized online allocation problem, a variant that includes a nonlinear regularizer acting on the total resource consumption. In this problem, requests repeatedly arrive over time, and for each request, a decision-maker needs to take an action that generates a reward and consumes resources. The objective is to simultaneously maximize additively…

  PublisherDOI

• cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia

  Operations Research · 2025-10-01 · 2 citations

  article1st authorCorresponding

  The paper cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia, by Haihao Lu and Jinwen Yang, addresses a fundamental question in large-scale optimization: Can modern GPUs be effectively leveraged for linear programming? The authors introduce cuPDLP.jl, a GPU-based solver implementing a restarted primal-dual hybrid gradient method entirely in Julia. Through extensive benchmarking on standard LP test sets, including MIPLIB relaxations and Mittel…

  PublisherDOI

• Optimizing Scalable Targeted Marketing Policies with Constraints

  Marketing Science · 2025-03-20 · 2 citations

  article1st authorCorresponding

  This paper introduces a novel optimization algorithm to address targeting problems with a large and complex set of constraints.

  PublisherDOI

• On the geometry and refined rate of primal–dual hybrid gradient for linear programming

  Mathematical Programming · 2024-07-17 · 2 citations

  articleOpen access1st authorCorresponding

  Abstract We study the convergence behaviors of primal–dual hybrid gradient (PDHG) for solving linear programming (LP). PDHG is the base algorithm of a new general-purpose first-order method LP solver, PDLP, which aims to scale up LP by taking advantage of modern computing architectures. Despite its numerical success, the theoretical understanding of PDHG for LP is still very limited; the previous complexity result relies on the global Hoffman constant of the KKT system, which is known to be very…

  PublisherOA PDFDOI

Frequent coauthors

• Vahab Mirrokni

  26 shared

• Santiago Balseiro

  12 shared

• Robert M. Freund

  Massachusetts Institute of Technology

  10 shared

• Miles Lubin

  Google (United States)

  8 shared

• David Applegate

  8 shared

• Jinwen Yang

  8 shared

• Benjamin Grimmer

  7 shared

• Mateo Díaz

  6 shared

Labs

• MIT SloanPI

Awards & honors

• 2026 Sloan Research Fellowship
• 2024 COIN-OR Cup
• 2024 Beale—Orachard-Hays Prize for Excellence in Computation…
• INFORMS Revenue Management and Pricing Section Prize (2023)
• Michael H. Rothkopf Junior Researcher Paper Prize (2022)

Similar researchers at Massachusetts Institute of Technology

• Dimitris Bertsimash-91
• James B. Orlinh-60
• Thomas Magnantih-44