Eugene Wu
Columbia University, NY·Computer Science
Charts from tools like Tableau or Excel can only use data that has been squashed into a single table. Most real data, however, lives in relational databases.
- Fields
Computer Science
- Topics
data visualization
database visualization
relational databases
- Grants
- 1 known grant, through Sep 2030
What they work on
- Develop a formal theory that defines visualizations as constraint-preserving mappings from relational database structure, including schemas and foreign keys, to visual structure, guaranteeing that charts soundly reflect the data.
- Build a compositional algebra over multi-view visualizations that supports constraint-aware transformations and symbolic reasoning about charts.
- Create dvl, a JavaScript visualization library for in-browser databases, with a compiler that translates visual specifications into optimized, incremental SQL pipelines for large datasets.
- Build a Visual Data Pool Analytics system that maintains sound, up-to-date visualizations as analysts curate tables, and evaluate it through benchmarks, user studies and real-world deployments.
More about the research
These are sets of linked tables that record relationships, such as which patients have which diagnoses. Flattening that data to make a chart throws away its structure. The result can be misleading charts, hidden errors and wrong conclusions. This project aims to build a foundation for visualizing relational databases directly, so that what a chart shows faithfully matches the data.
Why it matters
More accurate visualization tools could help scientists, policy makers and the public explore complex linked data without specialized technical skills. These tools would also reduce the risk of misleading charts and hidden errors. Planned deployments in biology and disaster preparedness show where more trustworthy charts could directly support decisions.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Alexander Kale
University of Chicago, IL·Computer Science·Early-career
Develop a theory that defines visualization design goals as trade-offs among kinds of information loss, made computable through mathematical formalisms derived from example analysis, synthesis and expert interviews.
Khairi Reda
University of Illinois Chicago, IL·Computer Science·Early-career
Extend image-computable models of human texture perception to chart and map images, predicting which data properties the early-to-mid visual cortex retains for the viewer.
Haeyong Chung
University of Alabama in Huntsville, AL·Computer Science·Early-career
Analyze existing multi-display applications in the academic literature to derive design knowledge on how multiple displays and devices can form one coherent visualization system for visual analysis.
The school
Columbia University: 89 labs on PI Pages, $1.4B a year on research, 625 research doctorates a year.
Reach out
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