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With Matthew Kay (Asst Prof. CS & Communication @ Northwestern University).
Wednesday, October 21, 2020 at 11:30 AM   Absolutely Free   Online

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About the speaker
Matthew Kay is an Assistant Professor in Computer Science & Communication at Northwestern University working in human-computer interaction & information visualization. His research areas include uncertainty visualization & the design of human-centered tools for data analysis. He is intrigued by domains where complex information, like uncertainty, must be communicated to broad audiences, as in health risks, transit prediction, or weather forecasting. He co-directs the Midwest Uncertainty Collective ( with Jessica Hullman, & is the author of the tidybayes ( & ggdist ( R packages for visualizing Bayesian models & uncertainty.

Bayesian modeling & effective uncertainty visualization are a natural pair: Bayesian modeling techniques produce samples from joint probability distributions that describe the uncertainty in estimates & predictions, & a growing body of research suggests that sampling-based visualizations of uncertainty can lead to better estimates & better decisions from users. In this talk I will tour a variety of modern uncertainty visualization techniques, discussing systematic principles for matching uncertainty encodings to the communication & decision goals of an uncertainty visualization, grounded in perceptual & cognitive aspects of uncertainty visualization understanding.

Along the way, I will demonstrate programming techniques for easily constructing complex uncertainty visualizations using Stan/brms with the tidybayes ( & ggdist ( R packages, which are designed specifically for creating uncertainty visualizations. Examples will be drawn from existing vignettes (e.g. &, my repository of uncertainty visualization examples (, & the uncertainty visualization literature (e.g. medical risk communication, hurricane path prediction, & real-time transit arrival prediction).

The goal of the talk will be to give the audience some grounding in the basic principles of effective uncertainty visualization design & then demonstrate how these principles can be applied to visualizing uncertainty from Stan models using APIs expressly designed for that purpose.

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