I am a fifth-year Ph.D. student majoring in computer science at the Institute for Interdisciplinary Information Sciences in Tsinghua University. I am very fortunate to be advised by Professor Andrew Chi-Chih Yao, who is the recipient of the 2000 A.M. Turing Award. I received my B.S. degree in artificial intelligence from Peking University in 2021, advised by Professor Liwei Wang.

My research lies at the intersection of theoretical and applied machine learning. On the theoretical side, I am interested in establishing provable guarantees for the generalization and optimization of machine learning algorithms. On the empirical side, I have hands-on experience with large-scale LLM pre-training and am committed to designing efficient optimization algorithms that improve scalability and performance in pre-training. I also have in-depth practical experience in quantitative research and have interned at top-tier quantitative trading firms, including Citadel Securities and Jump Trading.

My previous work includes:

• Efficient and stable optimizers for LLM pre-training.
• Adaptation of LLMs, e.g., parameter-efficient fine-tuning and scalable model merging.
• Generalization guarantees, implicit bias, and corresponding empirical signals in machine learning.
• Upper and lower convergence bounds for optimization algorithms on structured problems.

Internship Experiences

• Citadel Securities (Jun. 2025 – Sept. 2025)
  Quantitative Research Intern
  Built LLM pipelines to extract signals and build alphas from text-based alternative dataset. Received return offer.

• Moonshot AI (Feb. 2025 – Jun. 2025)
  Machine Learning Intern at Pre-training Team
  Developed efficient and stable optimization algorithms (e.g., Muon and its variants) for LLM pre-training.

• Jump Trading (Jun. 2024 – Aug. 2024)
  Quantitative Research Intern
  Conducted alpha analysis for China’s stock market.

Publications