I am currently the Ginni Rometty Associate Professor of Computer Science at Northwestern University. 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. My work has been awarded with multiple best paper and honorable mention awards at top visualization and HCI venues, a Microsoft Faculty award, a Google Faculty award, and NSF CAREER, Medium, and Small awards as PI, among others. I frequently speak and blog on topics related to data-driven decision making and interactive interfaces.

Selected recent publications

Zhang, D.*, Hartline, J., and Hullman, J. Designing Shared Information Displays for Agents of Varying Strategic Sophistication. ACM CSCW 2024. preprint

Wu, Y., Guo, Z.*, Mamakos, M., Hartline, J., and Hullman, J.. The Rational Agent Benchmark for Data Visualization. IEEE TVCG (Proc. VIS), 2023. preprint

Gelman, A., Hullman, J., and Kennedy, L. Causal quartets: Different ways to attain the same average treatment effect. American Statistician, Oct. 2023. preprint

Nanayakkara, P.*, Bater, J., Hu, X., Hullman, J., and Rogers, J. Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases. PoPETS 2022. preprint

Kale, A.*, Wu, Y., and Hullman, J. Causal Support: Modeling Causal Inferences with Visualization. IEEE TVCG (Proc. VIS), 2021. Best Paper Honorable Mention Award. PDF

Kale, A.*, Kay, M., and Hullman, J. Visual Reasoning Strategies for Effect Size Judgments and Decisions. IEEE TVCG (Proc. VIS), 2020. Best Paper Award. PDF

Hullman, J. Why Authors Don't Visualize Uncertainty. IEEE TVCG (Proc. VIS), 2019. PDF

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


* denotes student advisee

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