Toward Smarter Interactions with Data

Information visualizations leverage perception to summarize data in a cognitively efficient format, making them popular in science, analysis, and the media. 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 mission is to help more people make sense of complex information, and in particular to reason about data under uncertainty.

My research develops novel interactive tools and techniques that extend and amplify users' abilities to think with data. I do so by seeking data and interface abstractions that align with people's natural, internal representations of complex phenomena like probability or measurement. I also draw heavily on theories of statistical inference. My recent work has contributed interfaces and formal models that apply Bayesian inference to data analysis and interpretation, techniques for representing uncertainty in data as discrete sets of outcomes over time or space, algorithms and interfaces that use analogies to help people make sense of large measurements, and constraint and cost-based approaches to support automated reasoning about what makes a set of visualizations like a dashboard or sequence effective.

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 improving visualizations and data interfaces by leveraging theory and empirical findings on judgment under uncertainty. 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 a number of talented Ph.D. students including Yea-Seul Kim, Alex Kale, Paula Kayongo, Hyeok Kim, Priyanka Nanayakkara, Abhraneel Sarma, and Dongping Zhang.

Selected Recent Publications

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

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

Kale*, A., Kay, M., and Hullman, J.. Decision-Making Under Uncertainty in Research Synthesis: Designing for the Garden of Forking Paths. ACM CHI 2019. PDF

Hullman, J., Qiao*, X., Correll, M., Kale*, A., and Kay, M. In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation. IEEE VIS 2018. PDF

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

* denotes student advisee

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