Monday / CS Seminar

October 31st / 12:00 PM

Hybrid / Mudd 3514

Title: Designing, de-"bias"ing, and de-probabilizing uncertainty visualization

Speaker: Matthew Kay, Northwestern University

Zoom: https://northwestern.zoom.us/j/94158854527

Live Stream: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=945f67db-b0e5-4b77-9d80-af34015fb1ca

Abstract:

When visualizing data, we must consider carefully how to communicate uncertainty in ways that people can actually use to make informed decisions. I will discuss ways to combine knowledge of visualization perception, uncertainty cognition, and task requirements to design visualizations that more effectively communicate uncertainty. I will also discuss ongoing work in systematically characterizing the space of uncertainty visualization designs, tools for putting effective uncertainty communication into practice, and ways to communicate (difficult- or impossible-to-quantify) uncertainty in the data analysis process itself. I will also discuss ongoing and future work, e.g. work on improving election forecast visualizations and work on measuring and mitigating visualization misinformation.

Biography:

Matthew Kay is an Assistant Professor jointly appointed in Computer Science and Communications Studies at Northwestern University. He works in human-computer interaction and information visualization; more specifically, his research areas include uncertainty visualization, personal health informatics, and the design of human-centered tools for data analysis. His current research is funded by multiple NSF awards, and he has received multiple best paper awards across human-computer interaction and information visualization venues (including CHI, InfoVis, UbiComp, and MobileHCI). He co-directs the Midwest Uncertainty Collective (http://mucollective.co) and is the author of the tidybayes (https://mjskay.github.io/tidybayes/) and ggdist (https://mjskay.github.io/ggdist/) R packages for visualizing Bayesian statistical model output and uncertainty.