I build AI agent systems.

I’m a Senior Staff Researcher at Alibaba, based in Bellevue, Washington. Our team works on two fronts: probing the limits of what LLMs can do through new applications and agent harness design, and making agents production-ready through training platforms and secure runtimes. Current projects include QwenPaw, a secure, self-improving personal assistant; DojoZero, a platform for AI agents that reason and act on real-time data — predicting sports outcomes, for example; and TuFT, a multi-tenant platform for fine-tuning LLMs through a unified API. Our work is open source under the AgentScope organization.

Previously, I was a Principal Researcher at Microsoft Research in Redmond, Washington, where I served as the lead architect of AutoGen, an open-source framework for building AI agents and multi-agent applications. AutoGen offers a high-level API for orchestrating agents that collaborate autonomously or with human supervision, an event-driven, low-level API for full control over agent behavior, and a distributed runtime, built on the Actor Model, that hosts agents written in different programming languages. AutoGen grew into a widely adopted tool for agent developers and researchers, shaped by a community of over 500 contributors.
AutoGen:

I also worked with Azure AI to build the Foundry Agent Platform, a cloud platform for deploying AI agents and workflows at scale. As part of this effort, I helped create the Microsoft Agent Framework, an evolution of AutoGen for enterprise-grade applications, which now sits at the platform’s core.
Agent Framework:

Earlier at Microsoft Research, I worked on database systems. My collaborators and I developed a cost-based, platform-independent rewrite rule for MATCH_RECOGNIZE queries in general-purpose SQL engines, achieving a 5.4X median latency improvement in Trino. We then built a specialized execution engine for MATCH_RECOGNIZE with extended operators and a novel cost-model-based optimizer, delivering a 6X median performance gain over state-of-the-art engines.

Before joining Microsoft Research, I completed my PhD in Computer Science at the University of Toronto, advised by Prof. Renée J. Miller. My thesis focused on dataset search over massive Open Data archives. I contributed algorithms for large-scale set similarity search and data sketches, capable of searching over 100K tables for joinable or unionable ones in milliseconds. I also built an Open Data search engine stack to make Open Data more accessible for downstream applications.
SetSimilaritySearch:
datasketch:

I build in the open. Through open-source projects and writing, I aim to make advanced AI and algorithms accessible to everyone.

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