Making Data & Science Simpler

My mission is to help more people make sense of complex information through tools that use visualization, structured data, and automation to contextualize data and scientific information. To do so, my research in visualization develops tools and methods that we can use to make visualizations and other aids to understanding available in online contexts where data is encountered.

Brief Bio

I am an Assistant Professor in the iSchool at University of Washington and an adjunct Assistant Professor at UW CSE, where I research and teach interactive information visualization. I am a member of the Interactive Data Lab and the DataLab. Before this I was a postdoc at UC Berkeley Computer Science, working with Maneesh Agrawala (supported by Tableau Software). My Ph.D. and MSI are from the University of Michigan School of Information, where I worked with Eytan Adar.

I advise some amazing students including Yea-Seul Kim, Zening Qu, Alex Kale, and Francis Nguyen.

Recent Publications

Hullman, J., Qiao*, X., Correll, M., Kale*, A., and Kay, M. In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation. IEEE VIS 2018. Download PDF

Kale*., A., Nguyen*, F., Kay, M., and Hullman, J.. Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data. IEEE VIS 2018. Download PDF

Hullman, J., Kim*, YS., Nguyen*, F., Speers, L., and Agrawala, M. Improving Comprehension of Measurements Using Concrete Re-expression Strategies. ACM CHI 2018. Download PDF

Fernandes, M., Walls, L., Munson, S., Hullman, J., and Kay, M. Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making. (Honorable Mention) ACM CHI 2018. Download PDF

Qu* Z. and Hullman, J.. Keeping Multiple Views Consistent: Constraints, Validations, and Exceptions in Visualization Authoring (Honorable Mention) IEEE InfoVis 2017. Download PDF

* denotes student advisee

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