Trustworthy Information Lecture Series: Jessica Hullman
Jessica Hullman is Ginni Rometty Associate Professor of Computer Science at Northwestern University. Her research addresses challenges and limitations that arise when people draw inductive inferences from data. Her research on uncertainty representation and interactive data analysis explores how to best align data-driven interfaces and summaries with human reasoning capabilities, how to understand the role of interactive analysis across different stages of a statistical workflow, how to evaluate data interfaces as well as the experiments researchers use to identify differences between them, and how to develop tools that support reasoning under uncertainty in domains like strategic games or privacy.
Jessica's work has been awarded with multiple best paper and honorable mention awards at top visualization and HCI venues, a Microsoft Faculty award, a Google Faculty award, and NSF CAREER, Medium, and Small awards as PI, among others. She frequently speak and blog on topics related to visualization and reasoning about and representing uncertainty in data analysis and data-driven science.
Prior to joining Northwestern, Jessica was an assistant professor in the University of Washington School of Information and an adjunct assistant professor at UW CSE, where she was a member of the Interactive Data Lab and the DataLab. In 2014 she was a postdoc at UC Berkeley Computer Science, working with Maneesh Agrawala (supported by Tableau Software). She received my Ph.D. (2014) from the University of Michigan School of Information, where she worked with Eytan Adar.
If you have questions about this event, please contact Denise Simard.
If you require an accommodation for effective communication (ASL interpreting, CART captioning, alternative media formats, etc.) or information about mobility access in order to fully participate in this event, please contact Catherine Cronquist Browning with as much advance notice as possible and at least 7–10 days in advance of the event.