Master's student at Zhejiang University
Exploring Large Language Models, AI for Science, and intelligent methods for lithium-ion battery research.
I am a master's student at Zhejiang University, interested in the intersection of artificial intelligence and scientific research.
My current interests include:
- Large Language Models: model fundamentals, training, evaluation, and retrieval-augmented generation
- AI for Science: scientific reasoning and knowledge discovery
- Lithium-ion Batteries: data-driven and AI-assisted research methods
I enjoy turning complex ideas into implementations, experiments, and well-structured technical notes.
- Studying Stanford CS336: Language Modeling from Scratch
- Implementing tokenizers, Transformer components, optimizers, and training pipelines
- Organizing coursework, experiments, and lecture notes into public, reproducible repositories
- Exploring how language models can support scientific and battery research
| Track | Focus | Status |
|---|---|---|
| CS336 Assignments | Language models from first principles | In progress |
| Course Notes | Concepts, derivations, diagrams, and implementation details | Published |
| AI for Science | Scientific reasoning and knowledge discovery | Exploring |
| Battery Intelligence | AI-assisted lithium-ion battery research | Exploring |
CS336 2026 Notes — curated, systems-first notes covering tokenization, GPU kernels, distributed training, scaling, data, post-training, RLVR, and multimodality.
The repository is intentionally selective: it publishes original explanations and study structure, while linking back to the official Stanford CS336 materials instead of redistributing course assets.
Python · PyTorch · NumPy · Git · uv · pytest
Understand the idea, build the smallest version, test the assumptions, and explain what happened.
Building a bridge between language models and scientific discovery.