Haeyong Chung
University of Alabama in Huntsville, AL·Computer Science·Early-career
People now work among many screens at once, from smartwatches and phones to tablets and wall-sized displays. Using several displays together could help people explore data and make sense of it better than a single screen allows.
- Fields
Computer Science
Psychology
- Topics
data visualization
visual analytics
sensemaking
- Grants
- 1 known grant, through Apr 2027
What they work on
- 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.
- Develop display ecologies, sets of different displays that work in concert, that let users flexibly assign each display's role, such as views, visual representations or interfaces, for different visual analysis tasks.
- Build an augmented reality infrastructure that supports the spatial placement and physical handling of multiple displays within a display ecology.
- Integrate these components into one visual analysis system and evaluate, qualitatively and quantitatively, how multiple displays affect people's data analysis and sensemaking.
More about the research
Doing this well is hard. Designers have to decide how to combine different kinds of displays, how to spread data and charts across them, and how to help people connect what they see on separate screens. This project develops the idea of a "display ecology," in which different displays work together as one system for analyzing data.
Why it matters
Analysts, scientists and students who work with growing volumes of data could gain tools that spread visualizations across phones, tablets, wall displays and augmented-reality headsets in ways that help them find insights. The design knowledge and evaluation methods from this project could guide developers building multi-device visualization tools for many fields that depend on data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Michael Gleicher
University of Wisconsin-Madison, WI·Computer Science
Determine how people interpret small subsets of data items to grasp an overview of a large collection, using empirical user studies that link subset design choices to task performance.
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.
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 Alabama in Huntsville: 4 labs on PI Pages, $154M a year on research, 45 research doctorates a year.
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