Faculty Directory
Jessica Hullman

Associate Professor of Computer Science

Associate 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

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 as they use data. Information visualizations leverage perception to summarize data in a cognitively efficient format, making them popular in science, government, and the media. However, many visualizations and other data summaries fail to communicate effectively. One problem is that authors often omit uncertainty information in favor of optimizing the design of visualizations for pattern finding. As a result, conclusions from data are often believed to be more credible than they are. Other challenges arise when visualization use in the world “outgrows” the assumptions under which visualization tools and design knowledge were developed. For example, assumptions that visualizations are typically used in isolation for analysis of large datasets on desktop computers lead to a lack of sufficient tooling for helping authors negotiate design trade-offs that arise when visualizations are comprised of multiple related views, will be viewed on a range of devices, or are intended to communicate a set of specific points.

My research develops novel interactive tools and techniques that aim to extend and amplify users' abilities to reason under uncertainty when working with data. I achieve this by identifying abstractions that better align with people’s natural internal representations of complex phenomena, while remaining grounded in theories of statistical reasoning. My work has contributed techniques, theory, and systems related to uncertainty visualization, Bayesian inference, automated construction of visualizations for communication, and measurement analogies, among others.

Selected Publications

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

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

    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.