Conglei Shi is a founder and software engineer with nine years of experience building data-driven products and AI systems from research prototypes to production. With a Ph.D. in Information Visualization from HKUST and stints at IBM Research and Airbnb (data visualization and anomaly detection), he blends deep academic grounding with hands-on engineering. He has contributed to major open-source projects like Apache Superset, improving chart components and table visualizations to ensure parity with backend features and smoother UI rendering. Based in San Francisco, he has co-founded AI startups and now leads StoneFrontier, focused on pragmatic human–AI collaboration. Known for fixing subtle visualization bugs and unifying metric formats, he brings a detail-oriented approach to complex frontend and full-stack data challenges.
9 years of coding experience
7 years of employment as a software developer
B.S Computer Science, B.S Computer Science at Shanghai Jiao Tong University
Hong Kong University of Science and Technology (HKUST)
Apache Superset is a Data Visualization and Data Exploration Platform
Role in this project:
Full-stack Developer
Contributions:35 commits, 26 PRs, 33 comments in 1 year 7 months
Contributions summary:Conglei made several contributions focused on improving the user interface and fixing bugs within the Apache Superset platform. Their work involved modifying JavaScript files related to data visualization, specifically addressing issues with percentage formatting and tooltip rendering in visualizations. Additionally, they fixed a bug related to an icon in the table view and removed backend logic for wordcloud visualizations. Further, the user switched the table visualization to the new table component.
Contributions:13 commits, 12 PRs, 14 pushes in 11 months
Contributions summary:Conglei primarily contributed to the Superset UI packages, focusing on enhancements and bug fixes within the chart components. They modified chart plugin definitions and updated query object handling to accommodate additional fields, ensuring feature parity with the backend. Their work included unifying and reverting metric formats, fixing table column width calculations, and addressing rendering issues within the table visualizations, particularly involving HTML rendering and boolean values.
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