Khairi Reda
University of Illinois Chicago, IL·Computer Science·Early-career
People rely on charts, maps, and other data visualizations to make important decisions, from picking investments to reading medical scans. Yet visualizations can mislead.
- Fields
Computer Science
Psychology
- Topics
data visualization
information visualization
visual analytics
- Grants
- 2 known grants, through Sep 2029
What they work on
- 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.
- Validate model-based visualization evaluation against controlled human studies across chart and map types and tasks, and release it as open-source software and a biomedical tool for evaluating potential cancer treatments.
- Prototype techniques that incrementally capture analysts' beliefs and prior knowledge during visual analysis, distinguishing planned hypothesis testing from purely exploratory actions.
- Build and refine, through visual analysis studies and crowdsourced experiments, a model that forecasts the reliability of apparent patterns, then test real-time risk warnings in an RNA modification detection tool.
More about the research
A design may hide the pattern that matters, and interactive exploration can surface random noise that looks like a real trend. Two projects address how to make visual data analysis more reliable. One predicts how well a visualization works by simulating human vision. The other helps analysts tell real patterns from chance fluctuations.
Why it matters
Model-based evaluation could let designers test visualizations quickly and cheaply, improving charts used for public and scientific decisions, including tools for judging cancer treatments. Forecasting and flagging spurious patterns could help data analysts and bioinformatics researchers, such as those detecting RNA modifications, avoid false discoveries and trust their conclusions more.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Christopher Bryan
Arizona State University Campus Immersion, AZ·Computer Science
Develop a visualization grammar, a way to specify chart designs without low-level programming, that supports privacy constraints and data sanitization actions.
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.
Eugene Wu
Columbia University, NY·Computer Science
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.
The school
University of Illinois Chicago: 42 labs on PI Pages, $532M a year on research, 359 research doctorates a year.
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