Jessica Hullman, PhD

Assistant Professor in Computer Science and Journalism, Northwestern University

Jessica Hullman is an Assistant Professor in Computer Science and Journalism at Northwestern. The goal of her research is to develop computational tools that improve how people reason with and make decisions from data. She is especially interested in challenges that arise in presenting data to non-expert audiences, where the need to convey a clear story often conflicts with goals of transparency and faithful presentation of uncertainty. Her current research focus is on uncertainty representation through interactive visual interfaces that enable users to articulate and reason about their prior beliefs. Jessica’s research has been supported by a Microsoft Faculty Fellowship, and awards from NSF (CRII, CAREER), Navy, Google, Tableau, and Adobe.

Prior to joining Northwestern, she was an Assistant Professor at the University of Washington Information School. Her Ph.D. is from the University of Michigan and she spent a year as a Postdoctoral Scholar in Computer Science at the University of California Berkeley.

Talk Title  “Supporting Reasoning with Uncertainty Using Data Visualization”

Abstract Charts, graphs, and other information visualizations amplify cognition by enabling users to visually perceive trends and differences in quantitative data. While guidelines dictate how to choose visual encodings and metaphors to support accurate perception, it is less obvious how to design visualizations that encourage rational decisions and inference. Jessica will  motivate several challenges that must be overcome to support effective reasoning with visualizations.

First, people’s intuitions about uncertainty often conflict with statistical definitions. Jessica will describe how visualization techniques for conveying uncertainty through discrete samples can improve non-experts’ ability to understand and make decisions from distributional information.

Second, people often bring prior beliefs and expectations about data-driven phenomena to their interactions with data (e.g., I suspect support for candidate A is higher than reported), which influence their interpretations. Most design and evaluation techniques do not account for these influences. Jessica will describe what’s been learned by developing visualization interfaces that encourage users to reflect on their expectations and use them to predict and improve belief updating.

Alberto Cairo, PhD

Associate Professor | Knight Chair in Visual Journalism
UM School of Communication

Alberto Cairo is an Associate Professor and Knight Chair in Visual Journalism at the University of Miami. He is also director of the UM Frost Institute for Data Science and Computing Visualization program. Cairo spent more than a decade working in the news industry, as director of information graphics in publications in Spain and Brazil. He also works as a freelancer and permanent consultant for companies and government entities such as Google and the Congressional Budget Office. Cairo is the author of three books about visualization, ‘The Functional Art’ (2013), ‘The Truthful Art’ (2016), and the upcoming ‘How Charts Lie: Getting Smarter about Visual Information’ (October 2019).

Talk Title “How Charts Lie: What You Design is Not What People See”

Scientists, statisticians, designers, and journalists are often taught that “a picture is worth a thousand words”, that we should “show, don’t tell”, and that charts are “intuitive” and useful to “simplify” information. This talk explains why these myths, if taken at face value, are wrong and dangerous, and what we can do to help the public understand charts, graphs, maps, and infographics better. Contrary to what we commonly hear, there is always an uncertain gap between what we intend to communicate and what our readers end up seeing; bridging that gap should become a priority to any visualization designer.