Abstract: 
Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive visual data analysis, and data visualizations have become ubiquitous in communicative contexts like news and scientific publishing. However, despite these successes, our understanding of how to design robust visualizations for data-driven inference remains limited. For example, designing visualizations to maximize perceptual accuracy and users' reported satisfaction can lead people to adopt visualizations that promote overconfident interpretations. Design philosophies that emphasize data exploration and hypothesis generation over other phases of analysis can encourage pattern-finding over sensitivity analysis and quantification of uncertainty. I will motivate alternative objectives for measuring the value of a visualization, and describe design approaches that better satisfy these objectives. I will discuss how the concept of a model check can help bridge traditionally exploratory and confirmatory activities, and suggest new directions for software and empirical research.    

Bio: 
Dr. 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 interfaces. 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 theoretical frameworks for formalizing and evaluate the value of a better interface and elicitation of domain knowledge for data analysis. Hullman's work has been awarded best paper awards at top visualization and HCI venues. She is the recipient of a Microsoft Faculty Fellowship (2019) and NSF CAREER, Medium, and Small awards as PI, among others.