Speaker: Jessica Hullman (Northwestern University)

Location: Calvin Lab Auditorium & Zoom

Abstract: Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive visual data analysis as well as data communication. However, our understanding of how to design robust visualizations for data-driven inference remains limited by researchers’ heavy reliance on small user studies and hunches about the role of visual representations in inference. Using examples from recent visualization research, this talk will motivate the need for better-defined objectives and theoretical approaches for measuring the value of a visualization for supporting exploratory data analysis or communication. This talk will discuss recent work in progress at the intersection of visualization and theory.

Bio: Jessica Hullman is the Ginni Rometty Associate Professor of Computer Science at Northwestern University. Her research addresses challenges that arise when people draw inductive inferences from data summaries. Hullman’s work has contributed visualization techniques, applications, and evaluative frameworks for improving data-driven inference in applications like visual data analysis, data communication, privacy budget setting, and responsive design. Her current interests include how theorizing reasoning under uncertainty as mediated by representations of data could transform research and practice by providing insight into the value of a better interface. Hullman’s work has received best paper awards at top visualization and HCI venues, and she has received a Microsoft Research Faculty Fellowship and NSF CAREER, Medium, and Small awards as PI, among others.