Jessica Ruth Hullman

I am currently an Assistant Professor in the iSchool at University of Washington where I research and teach Information Visualization. I recently completed a postdoctoral fellowship at the University of California Berkeley in Computer Science, working with Maneesh Agrawala and supported by Tableau Software. I completed my Ph.D. with a focus in information visualization and HCI at the University of Michigan School of Information between 2009 and 2013, where I worked with advisor Eytan Adar. I also have an M.S.I. in Information Analysis & Retrieval from the University of Michigan School of Information and M.F.A. (Experimental Poetics and Prose) from the Jack Kerouac School for Disembodied Poetics at Naropa University.

I am passionate about visual analysis and communication around data. As used online by the media, scientists, and casual data analysts, information visualizations provide context and support deeper analytical insights related to data and text. Yet supporting the production of high quality visualizations is challenging. Design processes are complex and tacit and qualified professionals are scarce. As abstractions of complex data, visualizations can mislead if not designed carefully. My work focuses on deepening understanding of trade-offs that affect visualization practice, providing visualization techniques, systems, and knowledge frameworks to support more efficient production and interpretation.

I have worked extensively in the area of narrative visualization, or the use of statistical graphics to tell stories around data. I've demonstrated approaches for automatically generating and annotating visualizations to accompany news (most recently, news maps). I proposed visualization rhetoric as a framework for understanding persuasion through visualizations. I've studied how contextual factors like viewing order affect interpretations and proposed a graph-based algorithm for designing visualization sets..

I believe that better visualization based communication depends on understanding interpretation among diverse users. I am currently developing a generalizable framework for visualizing uncertainty in ways that even non-statisticians can easily understand. I've also investigated visual fluency effects, social influences, and crowd-based dynamics in visualization interpretation.