I lead the effort to build the agent foundation and self-improving harness from the ground up at an AI lab backed by top-tier VCs. My research focuses on long-context reasoning and the infrastructure needed to run advanced AI systems in production.
I work with academic institutions on interdisciplinary AI research, including AI for healthcare with the Shenzhen Institutes of Advanced Technology (SIAT).
Previously, I founded Miototech in China, one of the earliest AIGC companies, backed by Baidu VC, and Currents AI in the US, backed by VCs and part of NVIDIA Inception. Earlier in my career, I was an engineer at TikTok and AWS, where I built large-scale production systems from the ground up. At TikTok, I built core infrastructure from 0 to 1 that grew to support over 100 million daily active users; at AWS, I built services serving customers across 22 countries.
I am a cat dad to ChouChou
and Haru
, and a beginner tennis player.
Research
Self-Improving Harness
I build self-improving harnesses that close the loop between agent execution and learning. The harness turns runtime traces into structured feedback, evaluation, and iteration, so experience accumulates across tasks as durable capability gains rather than one-off completions.
AI for Healthcare
In collaboration with the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, I work on AI methods for healthcare. This research is supported by the Natural Science Fund of Guangdong Province.
AI-Native Infrastructure
I investigate infrastructure designed natively for AI agents rather than adapted from traditional web systems. This includes agent-oriented interfaces, structured interaction protocols that significantly reduce token consumption, and storage architectures integrating embeddings, graphs, and persistent memory to support scalable agent computation.
Long-Horizon Reasoning Systems
My work focuses on architectures for sustained reasoning over extended contexts and tasks. I study context engineering, graph-aware retrieval, multi-agent coordination, and memory mechanisms to enable structured, stateful inference beyond single-pass prompting.
Reliable AI Applications
I examine how to translate foundation models from benchmark settings into dependable production systems. This involves improving robustness, controllability, and observability, and grounding generation in structured signals to ensure reliability in real-world deployment.
Systems Built
- Currents Social Intelligence Engine (currents.one). Built an end-to-end social intelligence system for large-scale discussion analysis, integrating hierarchical long-context modeling, graph-aware retrieval, and emerging segment detection. Extracts structured product signals and user clusters from noisy social data. Launched on Product Hunt and ranked #1 of the day.
- Web Agent Harness. Built a web agent harness with GPT-5 that achieved state-of-the-art results. Proposed and implemented treating the browser as a code execution environment for agents, rather than simulating human UI actions — predating similar approaches later popularized by systems such as Browser Use.
- AWS SageMaker No-Code ML Platform. Built a no-code machine learning platform on Amazon SageMaker, enabling users to train, evaluate, and deploy models through a visual workflow instead of writing code.
- TikTok Live Stream Architecture. Early member of the TikTok Live engineering team. Led the 0 to 1 design and implementation of the core user authentication system for live streaming, supporting infrastructure at billion-scale daily active users.