Shiqian Ma

About

Shiqian Ma is a Professor of Computational Applied Mathematics and Operations Research at Rice University. His research areas include optimization and machine learning. He holds a Ph.D. from the Department of Industrial Engineering and Operations Research at Columbia University, obtained in 2011. Ma also earned a Master's degree from the Institute of Computational Mathematics and Scientific/Engineering Computing at the Chinese Academy of Sciences in 2006, and a Bachelor's degree from the School of Mathematical Sciences at Peking University in 2003. He is a member of the Ken Kennedy Institute and is involved in teaching operations research, optimization, and machine learning.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Mathematical optimization
• Algorithm
• Applied mathematics
• Pure mathematics
• Mathematical analysis
• Combinatorics

Selected publications

• A Riemannian Alternating Direction Method of Multipliers

  Mathematics of Operations Research · 2024-12-20 · 7 citations

  article

  We consider a class of Riemannian optimization problems where the objective is the sum of a smooth function and a nonsmooth function considered in the ambient space. This class of problems finds important applications in machine learning and statistics, such as sparse principal component analysis, sparse spectral clustering, and orthogonal dictionary learning. We propose a Riemannian alternating direction method of multipliers (ADMM) to solve this class of problems. Our algorithm adopts easily c…

  PublisherDOI

• AdaBB: Adaptive Barzilai-Borwein Method for Convex Optimization

  Mathematics of Operations Research · 2025-03-31 · 2 citations

  article

  In this paper, we propose AdaBB, an adaptive gradient method based on the Barzilai-Borwein stepsize. The algorithm is line-search-free and parameter-free, and it essentially provides a convergent variant of the Barzilai-Borwein method for general convex optimization problems. We analyze the ergodic convergence of the objective function value and the convergence of the iterates for solving general convex optimization problems. Compared with existing works along this line of research, our algorith…

  PublisherDOI

• Fully First-Order Methods for DecentralizedBilevel Optimization

  IEEE Transactions on Signal Processing · 2025-01-01 · 1 citations

  article

  This paper focuses on decentralized stochastic bilevel optimization (DSBO) where agents only communicate with their neighbors. We propose Decentralized Stochastic Gradient Descent and Ascent with Gradient Tracking (DSGDA-GT), a novel algorithm that only requires first-order oracles that are much cheaper than second-order oracles widely adopted in existing works. We further provide a finite-time convergence analysis showing that for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xli…

  PublisherDOI

• A Single-Loop Algorithm for Decentralized Bilevel Optimization

  Mathematics of Operations Research · 2025-10-03 · 1 citations

  article

  Bilevel optimization has gained significant attention in recent years because of its broad applications in machine learning. This paper focuses on bilevel optimization in decentralized networks and proposes a novel single-loop algorithm for solving decentralized bilevel optimization with a strongly convex lower-level problem. Our approach is built on the basis of the SOBA framework, and it is a fully single-loop method that approximates the hypergradient by using merely two matrix-vector multipl…

  PublisherDOI

• Demystifying Manifold Constraints in LLM Pre-training

  ArXiv.org · 2026-05-06

  articleOpen accessSenior author

  The empirical success of large language model (LLM) pre-training relies heavily on heuristic stabilization techniques, such as explicit normalization layers and weight decay. While recent constrained optimization approaches that explicitly restrict weights may improve numerical stability and performance, the mechanism and motivation for adding constraints still remain elusive. This paper systematically demystifies the role of explicit manifold constraints in LLM pre-training. By introducing the…

  PublisherOA PDF

Recent grants

• Collaborative Research: New Methods, Theory and Applications for Nonsmooth Manifold-Based Learning

  NSF · $99k · 2022–2024

• Collaborative Research: Distributed Bilevel Optimization in Multi-Agent Systems

  NSF · $250k · 2023–2027

• Collaborative Research: CIF: Small: New Theory and Applications of Non-smooth and Non-Lipschitz Riemannian Optimization

  NSF · $252k · 2022–2024

Frequent coauthors

• Shuzhong Zhang

  34 shared

• Donald Goldfarb

  22 shared

• Shixiang Chen

  Chang'an University

  15 shared

• Lingzhou Xue

  15 shared

• Tianyi Lin

  Columbia University

  14 shared

• Jiaxiang Li

  University of South China

  11 shared

• Bo Jiang

  10 shared

• Krishnakumar Balasubramanian

  University of California, Davis

  9 shared

Labs

• Shiqian Ma's LabPI

  Not provided

Similar researchers at Rice University

• Andrew J. Schaeferh-36
• Beatrice Riviereh-35
• Matthias Heinkenschlossh-34