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.
• 💼 At TikTok, 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, and retrieval-augmented biomedical summarization.
• 🌱 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.
• 💻 My broader research experience includes reinforcement learning for robotics, healthcare sequence modeling, privacy-preserving machine learning, and motion planning.
• ⛵ 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.

Explore my repositories, research interests, and technical writing, or reach out to discuss machine learning systems and frontier AI.