Hello, this is
Zhangchen Xu (徐张晨).
Bio
I am a third-year PhD student at Network Security Lab at the University of Washington, advised by Prof. Radha Poovendran. I’m also a part-time research intern at Microsoft GenAI, working with Dr. Yang Liu. Prior to UW, I graduated from University of Electronic Science and Technology of China (UESTC) and University of Glasgow (UofG) with a B.E. in Communication Engineering. During my undergrad, I was advised by Prof. Lei Zhang.
My email -> zxu9 [a-t] uw [d-o-t] edu
I am open to collaboration! Feel free to reach out if you would like to discuss Safety, Synthetic Data, and Post-training of LLMs, SLMs and VLMs~
Research Interests
My primary interests lie broadly in machine learning, networking, and security, with a current focus on the safety and alignment of large language models (LLMs). My current research directions include:
LLM Safety
I investigate emerging security threats in LLMs and explore defense mechanisms. I’m particularly interested in inference-time defenses.
- SafeDecoding is an inference-time defense against jailbreak attacks.
- CleanGen mitigates backdoor attacks for generation tasks in inference time.
- Shield defends against prompt-injection attacks in LLM-integrated Apps using cryptography.
LLM Alignment
I train language models to be more helpful and better align with human values with synthetic data. My current research focuses on distilling capabilities from powerful LLMs to smaller ones.
- Magpie is a framework that creates SOTA synthetic datasets from open-source LLMs.
- MagpieLM models are SOTA small language models for chat.
- Larger Models’ Paradox investigates the impact of response generators for synthetic dataset generation.
Distributed Algorithms
Federated Learning. Work includes ACE (contribution evaluation attack) and Brave.
Distributed Consensus. Work includes Voting Validity, Wireless Distributed Consensus, and Distributed Consensus Network.