I’m a Research Scientist at Apple MLR working on coding post-training and self-improving agents, including SSD, DuST, and RSI-Inference Bench. Previously, I developed speech representations and streaming generation methods, including dMel and SpeakStream.
Across these areas, I look for simple methods that address fundamental bottlenecks in how models learn and operate.
I completed my PhD in Computer Science at the University of Waterloo, with a dissertation titled "Novel Methods for Natural Language Modeling and Pretraining", under the supervision of Professor Ming Li. Prior to my doctoral studies, I obtained a Master's degree from the Chinese Academy of Sciences, where I began my NLP research journey under the mentorship of Professor Chengqing Zong.
*core contributors, †project lead
A long-horizon benchmark for coding agents that improve the inference stack of LLMs. An agent works on a real vLLM or SGLang engine, on real GPUs, across a model's life — day-0 support, post-launch optimization, and regression fixes — and is graded by running what it built.
Coding agents improve the vLLM and SGLang inference engines across 37 tasks: day-0 model support, optimization, and bug fixing.
*core contributors, †project lead
NeurIPS 2026
ACL 2026
IEEE ASRU 2025 (Best Demo)
*equal contribution
ACL 2024
EMNLP 2024
COLING 2024
*equal contribution
ACL 2024 Workshop (KnowLLM)
*equal contribution
Dissertation: Novel Methods for Natural Language Modeling and Pretraining
Findings of EMNLP 2022