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Da Chang

Pengcheng Laboratory, Shenzhen, China. 2025.7-Present

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 2024.9-Present

Ph.D. in Pattern Recognition and Intelligent Systems

Central South University, School of Automation, China. 2020.9-2024.6

B.Eng. in Intelligent Science and Technology

DL Research — Optimization • PEFT • Pre & Post Training

Da Chang

I graduated from the Department of Intelligent Science and Technology in 2024, School of Automation, Central South University. Currently, I am a jointly educated Ph.D candidate in a collaborative program between the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (SIAT) and Pengcheng Laboratory (PCL).
My major is Pattern Recognition and Intelligent Systems and my research interests focus on deep learning optimization and generalization and the application of deep models to various areas.
I am fortunate to be guided by Associate Professor Ganzhao Yuan and to maintain close contact with Researcher Yongxiang Liu and Researcher Huihui Zhou at Pengcheng Laboratory. I am very interested in the theory and application of deep learning. I would like to communicate with you about neural network training techniques, application scenarios and optimization theories of deep learning.

News

  • 2025-11 — Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT accepted —— Accepted at AAAI 2026.
  • 2025-11 — Preprint Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT posted.
  • 2025-09 — Preprint On the Convergence of Muon and Beyond posted.
  • 2025-09 — MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization —— Accepted at NeurIPS 2025 Spotlight.
  • 2025-07 — I started my second year of doctoral study in Pengcheng Laboratory.
  • 2024-09 — I started my first year of doctoral study in Chinese Academy of Sciences.
  • 2024-04 — Undergraduate graduation Project Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer —— Accepted at ICONIP 2024.
  • 2024-06 — I graduated from the School of Automation of Central South University.
  • 2024-06 — I won the outstanding undergraduate thesis of Central South University.

Selected Publications

On the Convergence of Muon and Beyond
Da Chang, Yongxiang Liu, Ganzhao Yuan
Preprint 2025.9 · PDF · Code
Analyzes Muon-type optimizers' convergence and extends the framework; The optimal complexity properties brought about by variance reduction are also discussed.
MuonConvergenceStochastic Optimization
Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT
Da Chang, Peng Xue, Yu Li , Yongxiang Liu, Pengxiang Xu, Shixun Zhang
AAAI 2026 · CCF A · PDF · Code
Analyze the properties of DoRA and LoRA, and unify the “calibration + rotation” weight conditioning strategy to enhance the performance and training-inference efficiency of PEFT.
LLMs PEFTWeight Conditioning
MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization
Da Chang, Ganzhao Yuan
NeurIPS 2025 Spotlight(Top 3%) · CCF A · PDF · Code
Provide ergodic convergence guarantees for stochastic nonconvex optimization and accelerate training via a momentum-gradient alignment strategy.
Stochastic OptimizationMomentumAlignment
Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer
Da Chang*, Yu Li*
(*:Equal contribution)
Undergraduate Graduation Project / ICONIP 2024 · CCF C · PDF · Code
TrOCR-based OCR with efficient PEFT for mixed text; practical pipeline and evaluation.
OCRTrOCRPEFT

Honors and Awards

  • Second prize, 8th National Biomedical Engineering Innovation Design Competition for College Students, China, 2023.
  • Third prize, 9th National Statistical Modeling Competition for College Students, China, 2023.
  • Second Class Scholarship, CSU (Top 15%), 2023.
  • "ShanHe Excellent Student" Second Class Scholarship, CSU (Top 5%), 2022.
  • First-Class Scholarship, CSU (Top 5%), 2022.

Skills

  • Languages: Python, C/C++, MATLAB, LaTeX
  • Tools: PyTorch, MindSpeed, scikit-learn, AutoML frameworks

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