I am a Senior Researcher at Microsoft Research, focusing on AI agents. I have also worked on database research.
My current project is AutoGen
,
an open-source framework for building AI agents and multi-agent systems.
AutoGen is a central hub that brings together agentic AI research and applications,
like PyTorch for deep learning. I am a core maintainer of the project.
My previous projects at Microsoft Research focused on query processing.
We developed a cost-based, platform-independent query plan rewrite rule for MATCH_RECOGNIZE
queries
in general-purpose SQL engines.
The new rule boosts median query latency by 5.4X in Trino.
In an even more ambitious undertaking, we developed a specialized execution engine for MATCH_RECOGNIZE
, which includes an extended set of operators and
a query optimizer based on a novel cost model. Our work resulted in a 6X median
performance improvement over state-of-the-art specialized execution engines.
Before joining Microsoft Research,
I completed my PhD in Computer Science at the University of Toronto,
under the guidance of
Prof. Renée J. Miller.
My thesis is in
dataset search over massive Open Data
archives. Specifically, I contributed algorithms for
large-scale
set similarity search
and data sketches
.
These algorithms can find joinable or unionable tables from over 100K tables in milliseconds.
Based on my research work, I built an Open Data search engine stack
to make it easy for people to
use Open Data in their applications.
I am constantly exploring new ideas in computing through my open-source projects and writings. My goal is to work towards the democratization of computation, whereby advanced A.I. and algorithms are easily accessible to everyone.
[Github] [Google Scholar] [Blog] [Email]