Speaker: Prof. G. Elisabeta Marai (University of Illinois Chicago )


Data visualization research often seeks to help solve real world problems across application domains, from biomedicine to engineering.
There is considerable merit in such endeavors, which often help advance knowledge in these application domains. Beyond these contributions, as we work alongside domain experts, we also have a unique opportunity to observe qualitatively and analyze how these clients interact with the data through our tools and paradigms. Thus, we have a rare opportunity to better ground data visualization theory on these observations. In this talk, I will examine how working with real world data and problems can point out specific gaps in our theoretical knowledge, can challenge underlying assumptions in the data visualization field, and can lead to new insights and theoretical guidelines. I will focus on several theoretical contributions grounded in this experience, from activity-centered design to visual scaffolding, the details-first paradigm, and visual explainability in artificial intelligence. Last, I will reflect on the lessons learned through this experience, with particular emphasis on the barriers our field poses to new theoretical contributions.


Liz Marai is an associate professor of Computer Science at the University of Illinois at Chicago. Her research interests go from visual-system related problems that can be robustly solved through automation, to problems that require human experts in the computational loop, and the principles behind this work. Marai's research has been recognized by multiple prestigious awards, including: a Test of Time award from the International Society for Computational Biology, and several Outstanding Paper awards, along with her students; an NSF CAREER Award and multiple NSF awards; and several multi-site NIH R01 awards as a lead investigator. She has co-authored scientific open-source software adopted by thousands of users, and she is a patent co-author, whose algorithms have been embedded in a medical device.