Undergraduate researcher studying optimization dynamics and numerical stability in large-scale machine learning systems.
The Chinese University of Hong Kong, Shenzhen
- Training dynamics in large-scale models
- Reduced-precision optimization
- Gradient geometry and trajectory alignment
- Stability analysis in efficient AI systems
I am currently investigating how reduced numerical precision influences optimization trajectories in LLM training.
Rather than focusing solely on final performance metrics, my work examines:
- Gradient deviation under precision perturbations
- Directional drift in optimization updates
- Structural sensitivity across model components (MoE vs Attention)
Selected repository:
🔗 Precision Effects on Training Dynamics
- DeepSeek-MoE large-scale training
- Multi-layer activation quantization experiments
- Gradient-level diagnostic analysis
- Optimization and mathematical modeling background
I am actively seeking summer research opportunities in the United States to further develop my research experience in ML Systems and Efficient AI.
My long-term goal is to pursue a PhD in the U.S., focusing on training dynamics, numerical stability, and optimization behavior in large-scale machine learning systems.
Email: 123090854@link.cuhk.edu.cn