Faculty Directory
Jessica Hullman

Associate Professor of Computer Science

Ginni Rometty Professor


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

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 data under 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. Beyond poor choices in how to visually encode data, a central problem is that authors often omit or downplay uncertainty information, such that data are interpreted as being more credible than they are.

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 like probability. Secondly, an interest in the mechanics and incentives of data interpretation and presentation motivates my use of controlled experiments to identify and model how people reason with data under uncertainty, as well as formal models to reason about limitations in current practice. I am particularly interested in how interfaces for reasoning with data and model predictions can be extended to support elicitation and modeling of users' prior beliefs.

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.