Biography

Hi there! I am Hanmin Li, a PhD candidate in Computer Science at KAUST under the supervision of Prof. Peter Richtárik. My research focus lies at the intersection of optimization and large language models (LLMs), with a focus on training efficiency and scalability. I am also interested in distributed training and the theoretical foundations of learning from decentralized data.

More broadly, my interests also extends to the theory of modern machine learning, including first-order methods, convex and non-convex optimization, and operator theory, as well as applied areas like deep learning and language modeling.

Before starting my Ph.D., I earned my master degress in Computer Science also at KAUST, after completing a B.S. in Computer Science and Technology at the School of the Gifted Young in the University of Science and Technology of China (USTC).

Currently, I am working on:

• Distributed training of large language models (LLMs), including experience with large-scale GPU clusters and training using PyTorch Distributed Data Parallel (DDP).
• Efficient optimizer design for large-scale training, with a focus on advancing the Muon optimizer and its variants to achieve faster convergence and improved scalability.
• Designing efficient algorithms for large language models (LLMs), with a focus on both theoretical analysis and empirical validation.

For any inquiries, feel free to contact me at hanmin.li@kaust.edu.sa.

Work Experience

• Applied Scientist Intern, Microsoft AI, June 2026 – September 2026.

Recent News

• Starting an Applied Scientist Internship at Microsoft AI — Jun 01, 2026
  

  I started a new position as an Applied Scientist Intern at Microsoft AI, running from June to September 2026.

• Talk at the ELLIIT Focus Period in Lund — May 12, 2026
  

  I gave a talk on Stabilizing Proximal Updates: Trust Regions, Linear Descent, and Connections to Modern ML Optimizers at the ELLIIT Focus Period Optimization for Learning in Lund.

• Attending NeurIPS 2024 — Dec 16, 2024
  

  This year, I will be attending NeurIPS in Vancouver, Canada.

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Papers

• Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
  Peter Richtárik, Kaja Gruntkowska, Hanmin Li, arXiv preprint. • [paper]
  

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• Broximal Alignment for Global Non-Convex Optimization
  Kaja Gruntkowska, Hanmin Li, Xun Qian, Peter Richtárik, arXiv preprint. • [paper]
  

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• Stabilized Proximal Point Method via Trust Region Control
  Hanmin Li, Kaja Gruntkowska, Peter Richtárik, arXiv preprint. • [paper]
  

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• The Ball-Proximal (=”Broximal”) Point Method: a New Algorithm, Convergence Theory, and Applications
  Kaja Gruntkowska, Hanmin Li, Aadi Rane, Peter Richtárik, arXiv preprint. • [paper] • [BibTex]
  

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• The Power of Extrapolation in Federated Learning
  Hanmin Li, Kirill Acharya, and Peter Richtárik., NeurIPS 2024. • [paper] • [BibTex]
  

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• On the Convergence of FedProx with Extrapolation and Inexact Prox
  Hanmin Li, and Peter Richtárik., NeurIPS 2024 OPT-ML Workshop Poster. • [paper] • [BibTex]
  

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• Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
  Hanmin Li, Avetik Karagulyan, Peter Richtárik, ICLR 2024. • [paper] • [BibTex]
  

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• Variance reduced distributed non-convex optimization using matrix stepsizes
  Hanmin Li, Avetik Karagulyan, Peter Richtárik, NeurIPS 2023 FL@FM Workshop. • [paper] • [BibTex]
  

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• SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information
  Haoyang Li, Hanmin Li, Juexiao Zhou, Xin Gao, Bioinformatics. • [paper] • [BibTex]
  

→ full list

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Talks

• Stabilizing Proximal Updates: Trust Regions, Linear Descent, and Connections to Modern ML Optimizers
  May 12, 2026 — ELLIIT Focus Period Optimization for Learning, Lund, Sweden
  Event

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• Poster Presentation of On the Convergence of FedProx with Extrapolation and Inexact Prox
  Dec 15, 2024 — NeurIPS 2024 OPT-ML Workshop, Vancouver, Canada
  

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• Poster Presentation of The Power of Extrapolation in Federated Learning
  Dec 11, 2024 — NeurIPS 2024, Vancouver, Canada
  

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• Talk of Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
  Jun 27, 2024 — EUROPT 2024, Lund, Sweden
  

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• Poster Presentation of Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
  May 07, 2024 — ICLR 2024, Vienne, Austria
  

→ full list

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Reviewer Services

• NeurIPS 24’, 25’
• NeurIPS OPT-ML 24’
• ICLR 25’
• ICML 25’
• JMLR
• IEEE TNNLS
• IEEE TSP
• Optimization Methods and Software.