Jessica Ruth Hullman

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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 understanding how people create, make sense of, and communicate with data and visualizations. 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. Professional designers construct useful visual aids for understanding data by carefully considering design trade-offs, yet their processes do not scale. For visualizations and other data summaries to be helpful calls for insight into how people reason with data, and tools that enable data understanding at scale, which is the goal of my work.

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..

My work is also driven to better understand the process of data cognition, especially around hard to grasp concepts like uncertainty and scales of measurement. I am currently developing a generalizable framework for visualizing uncertainty in ways that even non-statisticians can easily understand. I am also creating a large scale database and tools to support understanding of measurements, including making unfamiliar units easier to understand by relating them to more familiar objects. In the past, I've investigated visual fluency effects, social influences, and crowd-based dynamics in visualization interpretation.