I am a principal researcher in Microsoft Research at Redmond. My research is centered around the interplay between theories and systems related to data platforms and data science. I pursue fast, economic, scalable and practical solutions with theoretical guarantee. My research has led to life-changing technologies for data scientists and engineers. For example, I built FLAML, a fast library for AutoML and tuning. It is used widely inside and outside Microsoft, for example, in Visual Studio, Azure Data and Microsoft 365.
My recent work can be characterized under the emerging theme of MLOps (Machine Learning Operations) and MLSys (Machine Learning and Systems), such as:
- Fast and economical AutoML for training and inference
- Fast analytics of big data using learned data layout
- Accurate cardinality estimator with fast inference and training
- Fast, compact and updatable learned index
I completed my PhD from the Department of Computer Science, University of Illinois at Urbana-Champaign (UIUC), winning a SIGKDD Data Science/Data Mining PhD Dissertation Award in 2015. I had the pleasure to be advised by Professor Jiawei Han during the course of my PhD as a member of the Database and Information System (DAIS) Lab. I received my Bachelor’s degree in CS from Tsinghua University.