I am a research-oriented machine learning systems engineer working on foundation model infrastructure, alignment, and evaluation. I build efficient and reliable systems for large language models while studying the algorithms and data choices that make them more useful, controllable, and cost-effective in real applications.
- 🧐 My central research interest is model-system co-design: understanding how model architecture, inference algorithms, data curation, hardware utilization, scheduling, and distributed runtimes interact.
- 💼 I work on Model-as-a-Service platforms and high-performance LLM inference, developing production serving infrastructure with vLLM and SGLang.
- 🎓 My recent research includes distributed disaggregated inference, preference optimization, instruction-tuning data selection, multimodal evaluation.
- 🌱 I investigate alignment and evaluation methods that connect measurable model behavior with real-world usefulness, controllability, reliability, and serving cost.
- 📚 My systems work spans model runtime integration, scheduling and continuous batching, KV-cache and memory management, distributed execution, observability, and reliability.
- ⛵ I am interested in collaborating on open research and infrastructure that make frontier AI systems faster to experiment with, more rigorous to evaluate, and dependable at scale.
- ✍🏻 I share technical writing on machine learning systems, infrastructure, and software engineering through my personal blog.
Some other achievements about me~e~e
- 💙💛 Be proud of the University of California, Berkeley. 🐻 Proud California Golden Bear. Fiat Lux ✨ Go Bears.
- 🌲 Be proud of Stanford University. ❤️ Proud Stanford Cardinal. Die Luft der Freiheit weht.
- 🧣 Be proud of Carnegie Mellon University. 🐾 Proud Carnegie Mellon Tartan. My heart is in the work.
- 🎉 Professional Membership of ACM / IEEE / IEEE-CS / CCF / Sigma Xi.
- 🍎 Apple Developer.👨🏻💻 & Apple Teacher.🤪
- 📊 Open-source activity and repository highlights:
Explore my repositories, research interests, and technical writing, or reach out to discuss machine learning systems and frontier AI.
