Jessica Hullman

Jessica Hullman is the Ginni Rometty Professor of Computer Science at Northwestern University's McCormick School of Engineering and a Faculty Fellow at the Institute for Policy Research. Her research develops theoretical frameworks and interfaces for human-AI collaboration, focusing on uncertainty quantification, statistical modeling, and decision-making in domains like scientific research and AI-assisted analysis.

Education:

• PhD in Information (Visualization), University of Michigan (2013)
• MS in Information Analysis, University of Michigan (2008)
• BA in Comparative Studies, Ohio State University (2003)
• Tableau Postdoctoral Fellowship, UC Berkeley (2015)

Research Focus: Hullman's work bridges formal models of rational inference (e.g., Bayesian decision theory) with real-world applications. Key areas include: human-AI complementarity in decision-making, visualization of uncertainty, statistical reform, and LLM applications in behavioral science. Her research consistently addresses the alignment of data-driven interfaces with human cognitive capabilities.

Publication Trends: Recent work demonstrates a strong emphasis on human-AI collaboration frameworks, decision-theoretic evaluation of visualizations, and methodological rigor in machine learning and social science. Key themes include uncertainty quantification (conformal prediction, privacy tradeoffs), behavioral experiments in AI-assisted tasks, and critical analyses of scientific practices.

Awards & Honors:

• Microsoft Faculty Fellow (2019)
• Google Faculty Award
• NSF CAREER, Medium, and Small Awards
• Multiple best paper/honorable mention awards at top HCI/visualization venues (CHI, VIS)

Funding & Labs: Principal Investigator for NSF-funded projects including HCC: Medium on visualization tools. Previously affiliated with University of Washington's Interactive Data Lab and DataLab. Current research includes NSF-supported work on improving data visualization for reasoning about analytical assumptions.