I am currently the Ginni Rometty Associate Professor of Computer Science and a Faculty Fellow at the Institute for Policy Research at Northwestern University.
My research interests lie in how to address challenges that arise when people theorize and draw inductive inferences from data under uncertainty. I am interested in statistical workflows that span the explanatory and predictive (ML) modeling divide. My work on uncertainty visualization and interactive data analysis has explored how to best align data-driven interfaces and summaries with human reasoning capabilities to improve reasoning about data-generating processes, how to understand the role of interactive analysis across different stages of a statistical workflow, and how to develop tools that support reasoning under uncertainty in domains like strategic games or privacy. I maintain an active interest in metascience and statistical reform, and have contributed decision theoretic frameworks for better experiments for learning about the effects of data-driven inferences and model explanations. I approach many of these problems by drawing on formal models of rational inference as a basis for comparison and path toward 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