Hanyang Li

About

Hanyang Li

I am a Ph.D. candidate in Operations Research at UC Berkeley, advised by Ying Cui.

Before Berkeley, I earned an M.S. in Industrial Engineering from the University of Minnesota, Twin Cities, in 2023 and a B.S. in Mathematics and Applied Mathematics from the University of Science and Technology of China in 2021.

Email: hanyang_li AT berkeley DOT edu

Research Interests

My research focuses on optimization theory and algorithm design. I study how algorithms can make reliable progress on nonsmooth or ill-conditioned landscapes arising from implicitly defined objectives (e.g., value functions in parametric optimization and sequential decision-making). I also investigate how compressed representations affect optimization (e.g., quantization in memory-efficient model training and inference).

Publications and Preprints

Theory and Algorithms for Nonsmooth Optimization

  1. Subgradient Regularization: A Descent-Oriented Subgradient Method for Nonsmooth Optimization

    Hanyang Li, Ying Cui

    Submitted, 2025

    [code]

  2. Variational Theory and Algorithms for a Class of Asymptotically Approachable Nonconvex Problems

    Hanyang Li, Ying Cui

    Mathematics of Operations Research, 51(1), 1–34

    [slide]

    • Katta G. Murty Prize for Best Paper in Optimization, UC Berkeley (2025).
  3. A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse Functions

    Hanyang Li, Ying Cui

    SIAM Journal on Optimization, 34(1), 306–335

    [code] [slide]

    • Runner-up, Dupacova–Prekopa Best Student Paper Prize (2023).
    • Third Place, INFORMS JFIG Paper Competition (2022).

Optimization for Memory-Efficient Training and Inference

  1. Beyond Shadow Weights: Quantization-Aware Training as Quantized-Endpoint Descent

    Sheng-An Xu, Hanyang Li, Jianhao Ma, Ying Cui

    Submitted, 2026

  2. Understanding Quantization-Aware Training: Gradients at Quantized Weights Bias to the Low-Loss Basin

    Hanyang Li, Jianhao Ma, Ying Cui

    Submitted, 2026

  3. Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State Quantization

    Hanyang Li, Shao Tang, Daniel Braithwaite, Gregory Dexter, Leonardo Neves, Abhishek Shivanna, Aman Gupta, Hiroto Udagawa, Daniel Silva, Rohan Ramanath

    Submitted, 2026

    • Preliminary version appears in the NeurIPS 2026 Workshop on Optimization for Machine Learning (OPT 2026).

2026

  1. Beyond Shadow Weights: Quantization-Aware Training as Quantized-Endpoint Descent

    Sheng-An Xu, Hanyang Li, Jianhao Ma, Ying Cui

    Submitted, 2026

  2. Understanding Quantization-Aware Training: Gradients at Quantized Weights Bias to the Low-Loss Basin

    Hanyang Li, Jianhao Ma, Ying Cui

    Submitted, 2026

  3. Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State Quantization

    Hanyang Li, Shao Tang, Daniel Braithwaite, Gregory Dexter, Leonardo Neves, Abhishek Shivanna, Aman Gupta, Hiroto Udagawa, Daniel Silva, Rohan Ramanath

    Submitted, 2026

    • Preliminary version appears in the NeurIPS 2026 Workshop on Optimization for Machine Learning (OPT 2026).

2025

  1. Subgradient Regularization: A Descent-Oriented Subgradient Method for Nonsmooth Optimization

    Hanyang Li, Ying Cui

    Submitted, 2025

    [code]

  2. Variational Theory and Algorithms for a Class of Asymptotically Approachable Nonconvex Problems

    Hanyang Li, Ying Cui

    Mathematics of Operations Research, 51(1), 1–34

    [slide]

    • Katta G. Murty Prize for Best Paper in Optimization, UC Berkeley (2025).

2024

  1. A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse Functions

    Hanyang Li, Ying Cui

    SIAM Journal on Optimization, 34(1), 306–335

    [code] [slide]

    • Runner-up, Dupacova–Prekopa Best Student Paper Prize (2023).
    • Third Place, INFORMS JFIG Paper Competition (2022).

Talks and Presentations

  • INFORMS Annual Meeting · San Francisco, CASession: Optimization Methods for Quantizing and Pruning LLMs · Upcoming
    11/2026
  • Cornell ORIE Young Researchers Workshop · Ithaca, NYPoster presentation · Upcoming
    10/2026
  • INFORMS Optimization Society Conference · Atlanta, GASession: Recent Advances in Stochastic and Large-Scale Optimization
    03/2026
  • Hong Kong University of Science and TechnologyJoint IEDA/ISOM Seminar
    01/2026
  • Hong Kong Polytechnic UniversityAMA Seminar Series on Young Scholars in Optimization and Data Science
    01/2026
  • International Conference on Continuous Optimization · Los Angeles, CASession: First-order Methods for Nonsmooth Constrained Optimization
    07/2025
  • INFORMS Annual Meeting · Phoenix, AZSession: Recent Advances in Nonsmooth Optimization
    10/2023
  • XVI International Conference on Stochastic Programming · Davis, CAStudent Paper Prize Competition
    07/2023