Faculty Directory
Jessica Hullman

Associate Professor of Computer Science

Ginni Rometty Professor


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

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Jessica Hullman's website


Computer Science

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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

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

Research Interests

My interests lie in challenges and limitations that arise when people theorize and draw inductive inferences from data. My research on uncertainty representation and interactive data analysis explores how to best align data-driven interfaces and summaries with human reasoning capabilities, how to understand the role of interactive analysis across different stages of a statistical workflow, how to evaluate data interfaces as well as the experiments researchers use to identify differences between them, and how to develop tools that support reasoning under uncertainty in domains like strategic games or privacy. I approach many of these problems by drawing on formal models of rational inference as a basis for comparison and proposing solutions. 

Selected Publications

Hofman, J., Goldstein, D., Hullman, J. How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results. ACM CHI 2020. 

Hullman, J. Why Authors Don't Visualize Uncertainty. IEEE VIS 2019. 

Kim, YS., Walls, L., Krafft, P., Hullman, J. A Bayesian Approach to Improve Data Visualization. ACM CHI 2019.

Kim, YS,., Dontcheva, M., Adar, E., Hullman, J. Vocal Shortcuts for Creative Experts. ACM CHI 2019.

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.