Research

My research interests are in the fields of data management, data science, and data analytics related to AI and machine learning, including databases, data-intensive computing,  search, and large-scale analytics and visualization. My PhD thesis at Stanford was on data integration, with an emphasis on both theoretical and practical aspects. My recent research, especially after spending a few quarters at Google and a few years doing a startup as its founder and CTO, has a strong preference on engineering and open source system building.  I believe “Computer Science” is a “Science” to support great engineering, and we need to build systems to stay relevant in this fast-paced IT era. I was a co-PI of the Apache AsterixDB project. Since 2016, our team has been developing the Texera open-source system to support cloud-based collaborative data science, AI, and ML using workflows.

Current Projects

  • Apache Texera (Incubating): Human-AI Collaborative Data Science Using Visual Workflows (since 2016)
  • SqlRewriter: A platform for SQL users to share rewriting rules to improve query performance (since 2023).

Current Fundings

  • NSF IDSS Award “Category II: BRIDGE: A National Center to Democratize Multi-disciplinary Data Science and AI for Research Labs and Communities”. Total ~$9M for 3 campuses and 3 years.
  • NIH NIDDK Award “dkNET Coordinating Unit: Harnessing the Power of AI and Data Science forCollaborative Discovery and Sharing in the DK Community”. Total $10M for 4 campuses and 5 years.
  • A grant from SAP.

Selected Past Projects

  • Flamingo: A project on data cleaning and string similarity queries.
  • Ipubmed: Efficient instant search on large amounts of data.
  • Apache AsterixDB: An open source parallel database system for Big Data.
  • Cloudberry: A middleware system for Big Data visualization.

Released Prototypes and Source Code Packages