Faculty DirectoryJessica Hullman
Ginni Rometty Professor of Computer Science
Contact
2233 Tech DriveMudd Room 3521
Evanston, IL 60208-3109
Email Jessica Hullman
Website
Departments
Education
Postdoctoral Fellowship, Computer Science, University of California Berkeley, Berkeley, CA, 2015PhD in Information, School of Information, The University of Michigan, Ann Arbor, MI, 2013
Master of Science in Information, School of Information, The University of Michigan, Ann Arbor, MI, 2008
Bachelor of Arts, Comparative Studies. The Ohio State University, Columbus OH, 2003
Research Interests
My research develops methods for evaluating and improving AI and ML systems for human interaction for decision-making and scientific inference. My students and I study when predictions, explanations, uncertainty estimates, model-generated judgments, or other AI outputs provide valid and useful evidence for downstream decisions. Current work spans AI-assisted decision-making, human-AI and multi-agent complementarity, LLMs as behavioral evidence, and AI systems for scientific research and data analysis. I work between theory and application, grounding my contributions in formal models of rational inference like Bayesian decision theory while addressing applied problems in AI evaluation and deployment. I also maintain an active interest in metascience and statistical reform.
I direct the Epistemic Decisions Lab in CS.
Selected Publications
Guo, Z., Ustun, B., and Hullman, J. Explanations are a Means to an End: Decision Theoretic Explanation Evaluation. ICML 2026.
Guo, Z., Wu, Y., Hartline, J., and Hullman, J. Explaining and Improving Information Complementarities in Multi-agent Decision-making. ICLR 2026.
Hullman, J., Kale, A., and Hartline, J. Decision Theoretic Foundations for Experiments Evaluating Human Decisions. ACM CHI 2025.
Nanayakkara, P., Kim, H., Wu, Y., Sarvghad, A., Mahyar, N., Miklau, G., and Hullman, J. Measure-Observe-Remeasure: An Interactive Paradigm for Differentially-Private Exploratory Analysis. IEEE Symposium on Security and Privacy 2024.
Guo, Z., Wu, Y., Hartline, J., and Hullman, J. A Decision-Theoretic Framework for Measuring AI Reliance. ACM FAccT 2024.
Zhang, D., Chatzimparmpas, A., Kamali, N., and Hullman, J. Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling. ACM CHI 2024.
Wu, Y., Guo, Z., Mamakos, M., Hartline, J., and Hullman, J. The Rational Agent Benchmark for Data Visualization. IEEE TVCG 2023.
Gelman, A., Hullman, J., Kennedy, L. Causal Quartets: Different Ways to Attain the Same Average Treatment Effect. American Statistician 2023.