Alexander Kale
University of Chicago, IL·Computer Science·Early-career
Charts and graphs shape how scientists, journalists and the public understand data, but it can be hard to tell whether a chart presents the evidence honestly. Every visualization shows only part of a complex dataset.
- Fields
Computer Science
Education Research
- Topics
data visualization
information visualization
data disclosure
- Grants
- 1 known grant, through May 2031
What they work on
- 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.
- Build software that reasons automatically over possible chart designs and recommends options that balance goals such as clear communication and the privacy of data subjects.
- Create interfaces that generate explanations for audiences about what a chart cannot show by design, and evaluate them through user studies and controlled experiments.
- Design data disclosure games and teaching materials that let students practice responsible ways of handling information loss in visualizations.
More about the research
A chart that tries to show everything becomes unreadable, so designers must choose what to reveal and what to hide or distort. This project treats visualization as a form of data disclosure. Designing a chart then becomes a matter of deciding which kinds of information loss are acceptable for a given purpose.
Why it matters
Misleading or overly revealing charts can distort public understanding and expose private data about individuals. This work could give chart authors practical tools for choosing honest, privacy-aware designs, and help readers recognize what a chart leaves out. Its games and teaching materials aim to train students to communicate data ethically.
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.
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.
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.
The school
University of Chicago: 39 labs on PI Pages, $681M a year on research, 457 research doctorates a year.
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