Profile
Undergraduate in the SJTU ACM Class (2024–Present) at
Shanghai Jiao Tong University.
My current research focus is on
Large Language Models, especially their pre-training
and post-training. I actively keep up with the rapid developments in
the field and build side projects to learn new paradigms, protocols,
and programming languages.
Education
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Shanghai Jiao Tong University, B.S. Computer Science (ACM Class) 2024 - Present
GPA: 3.9 / 4.3
Research Experience
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GAIR Lab (plms.ai), Research Intern (Advisor: Dr. Pengfei Liu) Summer 2025
Researching Large Language Models with interests in mid-training and post-training stages. Working on providing efficient training data which incentivizes model's agentic capabilities.
Teaching Experience
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Teaching Assistant, Programming (C++) CS1953 Fall 2025
- Gave lectures on shell usage and Git workflows, and supported students in hands-on sessions.
- Provided an interactive learning platform (adapted from pwn.college) with challenge-based exercises to reinforce shell and Git concepts. Open-sourced our platform on: acmdojo and shell-dojo-core.
Selected Projects
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Networking Systems Summer 2025
- Transparent QUIC Proxy: Implemented a proxy layer to handle QUIC session forwarding and address translation.
- NeoFRP: Built a lightweight reverse proxy (Fast Reverse Proxy style) for tunneling internal services. 70% faster than original FRP in throughput under same setting.
- SOCKS5 Server with FullCone UDP: Added UDP FullCone behavior for broader NAT traversal and improved compatibility.
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RISC-V Simulator Summer 2025
A RISC-V simulator (RV32-I) with Tomasulo algorithm for out-of-order execution.
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vscode-acmoj Spring 2025
A simple integration of ACMOJ into VSCode, which allows you to browse problemsets (contests/homework), view problems, submit your code, and check submission results without leaving your editor.
Contributed
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verl: Volcano Engine Reinforcement Learning for LLMs (contributed).
Skills
- Languages: C++, Python, TypeScript, Go
- Interests: Large Language Model mid-training & post-training; networking protocols
Selected Coursework
- Mathematical Logic: 4.3 / 4.3
- Great Ideas in Computer Science: 4.3 / 4.3
- Principle and Practice of Computer Algorithms (PPCA): 4.3 / 4.3
- Data Structure: 4.0 / 4.3
- Mathematical Analysis: 4.0 / 4.3