Since July 2024, I have been a postdoctoral researcher at ARISE, The Chinese University of Hong Kong, mentored by Prof. Michael R. Lyu. My journey in tech was forged at Sun Yat-sen University, where I earned my Ph.D. and M.Eng under the guidance of Prof. Pengfei Chen. From 2022 to 2023, I was a research intern at Tencent’s WeChat Group through the CCF–Tencent Rhino-Bird Elite Program.
My path began not with code, but with traditional Chinese medicine. I started my academic career at Guangzhou University of Chinese Medicine, often called the "MIT of Traditional Chinese Medicine", with the dream of becoming a physician who diagnosed the "bugs" of human body. Today, I've channeled that diagnostic spirit from human systems to software systems, dedicating myself to identifying and healing the faults that affect software reliability and keeping our digital world healthy.
😃 Passionate about prescribing solutions for system reliability. If you're interested in building more robust software, let's connect!
News
🏆 Our paper PreServe received the ACM SIGSOFT Distinguished Paper Award at ICSE 2026. CUHK announcement ↗
🏆 Our paper iKnow received the ACM SIGSOFT Distinguished Paper Award at ASE 2025. CUHK announcement ↗
🎉 Our Alert Management and Cloud Ops RAG Paper has been accepted by ASE'25.
Earlier news
🎉 Our Network Telemetry RCA Paper has been accepted by ToN. Congras to Hongyang!
🎉 Our Cloud Incident Management Paper has been accepted by TSE. Congras to Zilong!
🎉 Our Survey on Failure Analysis and Fault Injection in AI Systems has been accepted by TOSEM.
🎉 Log-based LLM training failures diagnosis framework L4 has been accepted by FSE'25. Congras to Zhihan!
🎉 Code knowledge enhanced root cause analysis framework CoCA has been accepted by ICSE'25. Congras to Yichen!
🎉 Microservice Trace Framework Mint has been accepted by ASPLOS'25. Congras to Haiyu!
🎉 FaaS configuration optimization Paper FaaSConf has been accepted by ASE'24 (118/587). Congras to Yilun!
Two close readings: verifier strength and recoverability as conditions for agent self-improvement, and repairs for gradient errors hidden by rounding in softmax and cross-entropy.