Communicating Data & Uncertainty

My mission is to help more people make sense of complex information, and in particular to reason about uncertainty. Information visualizations leverage perception to summarize data in a cognitively efficient format, making them popular in the media and science. However, many visualizations and other data summaries fail to communicate effectively. One problem is that authors often omit uncertainty information, such as that data are interpreted as being more credible than they are. Another problem is that authors often assume that if the right information--data, statistic, finding etc.--is presented, the audience will naturally trust the presentation and make better decisions.

My research addresses these problems in two ways. First, as a computer scientist I create novel interactive tools and techniques that aim to extend and amplify users' abilities to think with data by aligning with their internal representations of complex phenomena. Secondly, an interest in the psychology of data interpretation and presentation motivates my use of controlled experiments to identify and model how people reason with data, and in particular uncertainty.

Brief Bio

I am currently the Allen K. and Johnnie Cordell Breed Assistant Professor of Computer Science & Engineering at Northwestern University, where I direct the MU Collective, a lab devoted to information visualization and uncertainty cognition. I am also an Assistant Professor in the Medill School of Journalism at Northwestern. From January 2015 to July 2018 I was an Assistant Professor in the iSchool at University of Washington and an adjunct Assistant Professor at UW CSE, where I was 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, 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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