Associate Professor of Computer Science, Northwestern University
- Friday, December 4, 2020 - 1:30pm to 3:00pm
- Seminar will be held virtually
- Seminar Video
"Beyond Visualization: Theories of Inference to Improve Data Analysis & Communication"
Data analysis is a decidedly human task. As Tukey and Wilk once wrote, “Nothing—not the careful logic of mathematics, not statistical models and theories, not the awesome arithmetic power of modern computers—nothing can substitute here for the flexibility of the informed human mind.” Research in supporting interactive and exploratory analysis has produced a number of sophisticated interfaces, many of which are optimized for easy pattern finding and data "exposure." However, visualization tools are often used by analysts and others to make inferences beyond the data, and as my own and others' research has shown, these inferences often deviate from the predictions of statistical inference. I'll describe how an absence of theories of inference that ground our understanding of how to design for interactive analysis may threaten the validity of conclusions people draw from visualizations, and describe what we've learned by using theories of statistical inference to better understand and design for intuitive visual analysis and communication.
- Speaker's Bio
Jessica Hullman is an Associate Professor of Computer Science with a joint appointment in the Medill School of Journalism at Northwestern University. Her research looks at how to design, evaluate, coordinate, and think about representations of data for amplifying cognition and decision making. She co-directs the Midwest Uncertainty Collective, a lab devoted to better representations, evaluations, and theory around how to communicate uncertainty in data, with Matt Kay. Jessica is the recipient of a Microsoft Faculty Fellowship, NSF CAREER Award, and multiple best papers at top visualization and human-computer interaction conferences.
- Speaker's Website
- Ken Holstein