Event Information:

  • Tue

    Jessica Hullman

    When and where:

    March 19th, 11:30 AM, North Quad, Ehrlicher Room (room 3100) (add to calendar)

    Light lunch will be provided at 11:30 AM. The talk will begin at noon.

    Please RSVP by 12 PM on 3/17 so we know how much food to order.


    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. I'll motivate several challenges that must be overcome to support effective reasoning with visualizations. First, people's intuitions about uncertainty often conflict with statistical definitions. I'll 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. I'll describe what we've learned by developing visualization interfaces that encourage users to reflect on their expectations and comparing elicited prior and posterior expectations to normative accounts of belief updating.
    Bio: 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 data. She is particularly inspired by how science and data are presented to non-expert audiences in data and science journalism, where the goal of conveying a clear story often conflicts with goals of transparency and faithful presentation of uncertainties. Her current research aims to develop uncertainty techniques and interactive visualizations that enable users to articulate prior beliefs and make more informed decisions. Jessica's research has been supported by the NSF (CRII, CAREER), Navy, Google, Tableau, and Adobe. Prior to joining Northwestern in 2018, she spent three years as an Assistant Professor at the University of Washington Information School. She completed her Ph.D. at the University of Michigan and spent a year as the inaugural Tableau Software Postdoctoral Scholar in Computer Science at the University of California Berkeley in 2014.