Faculty Directory
Jessica Hullman

Assistant Professor of Computer Science

Assistant Professor of the Medill School of Journalism

Breed Junior Professor of Design


2233 Tech Drive
Mudd Room 3521
Evanston, IL 60208-3109

Email Jessica Hullman


Jessica Hullman's website


Computer Science


Tableau Software Postdoctoral Fellowship, Computer Science Division, University of California Berkeley, Berkeley, CA, 2015 

PhD in Information (Visualization), The University of Michigan School of Information, Ann Arbor, MI, 2013 

Master of Science in Information (Information Analysis and Retrieval), The University of Michigan School of Information, Ann Arbor, MI, 2008

Master of Fine Arts in Writing and Poetics, Prose Concentration, Jack Kerouac School for Disembodied Poetics at Naropa University, Boulder, CO, 2006

Bachelor of Arts, Comparative Studies. The Ohio State University, Columbus OH, 2003

Research Interests

The goal of my research 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, I use of controlled experiments to identify and model how people reason with data, and in particular uncertainty. Secondly, 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.

Selected 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.

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

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

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

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

    Kim, YS, Reinecke, K., and Hullman, J. Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data. ACM CHI 2017. Best Paper Award 

    Kim, Y., Wongsuphasawat, K., Hullman, J., and Heer, J. Graphscape: A Model for Automated Reasoning About Visualization Similarity and Sequencing. ACM CHI 2017. Honorable Mention

    Hullman, J., Resnick, P., and Adar, E. Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences About Reliability of Variable Ordering. PLOS ONE 2015.