Data Visualization
Data-based evidence is in demand. Center researchers study how data analysis can be more powerful, and data communication more clear, when those data are depicted by well-designed visualizations. Our work tackles technical challenges, such as large datasets, differing screen sizes, or novel interaction tools. We explore visualization designs that more intuitively convey information, from the patterns within a dataset, to a sequence of patterns that tell a story. We create analysis systems that minimize decision-making bias, and help people understand the complexities of uncertainty and risk. This focus area draws on an intellectually diverse set of fields, including computer science, design, perception, statistical cognition, and reasoning.
Meet Our Faculty Experts
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Nick Diakopoulos Steven Franconeri Darren Gergle Mike Horn
Jessica Hullman Matt Kay
Example Publications
Zacks, J. M., Franconeri, S. L. (2020). Designing Graphs for Decision-Makers. Policy Insights from the Behavioral and Brain Sciences.
Pu, X., & Kay, M. (2020, April). A Probabilistic Grammar of Graphics. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems(pp. 1-13).
Kim, YS., Walls, L., Krafft, P., and Hullman, J. (2019) A Bayesian Cognition Approach to Improve Data Visualization. ACM CHI 2019.
Qu, Z. and Hullman, J. Keeping Multiple Views Consistent: Constraints, Validations, and Exceptions in Visualization Authoring. IEEE InfoVis 2017. Honorable Mention.