Jessica Ruth Hullman

I am a postdoctoral fellow at the University of California Berkeley in Computer Science, working with Maneesh Agrawala. My postdoc work is supported by Tableau Software Research. I completed my Ph.D. 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 news organizations, scientists, and data enthusiasts, information visualizations provide context and support deeper analytical insights related to data and text information. Yet supporting the production of high quality visualizations is challenging. Design processes are complex and tacit and professionals hard to find. As abstract representations, visualizations have the potential to mislead or bias interpretations 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 visualization production and intepretation among diverse audiences.

I have studied topics in narrative visualization, or the use of graphics to tell stories around data. I've demonstrated approaches for automatically generating and annotating visualizations to accompany news (see also forthcoming CHI 2014 paper). I developed a rhetorical framework for understanding how narrative visualizations persuade users to accept a given framing of data. I have studied the potential for viewing order to affect visualization interpretations and proposed a graph-based algorithm for helping designers create effective sets of visualizations for presentation..

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 have also studied factors influencing the interpretation of visualized data for learning and social and crowdsourcing environments.