Making Data & Science Simpler
My work aims to help more people make sense of complex information through tools that use visualization, structured data, and automation to contextualize data and scientific information. As used online by the media, scientists, and “casual” analysts, information visualizations and other interactive data tools provide context for otherwise complex information.
However, many of the most illuminating visualizations are created by expert designers. My research develops tools and methods that we can use to make visualizations and other aids to understanding available in online contexts where data is encountered, from reading the news, to researching facts online, to using social media. The specific goals of my work are to identify design principles used to create human-designed data representations, and to use these principles to develop automated and “human-in-the-loop” tools for producing and enhancing data presentations.
I am an Assistant Professor in the iSchool at University of Washington and an adjunct Assistant Professor at UW CSE, where I research and teach Information Visualization and HCI. I am a member of the Interactive Data Lab and the DataLab. Before this I was a postdoc at UC Berkeley Computer Science, working with Maneesh Agrawala (supported by Tableau Software). My Ph.D. and MSI are from the University of Michigan School of Information, where I worked with Eytan Adar.
Kim, YS, Hullman, J., Burgess, M., and Adar, E. SimpleScience: Lexical Simplification of Scientific Terminology. EMNLP 2016
Hullman, J. Why Evaluating Uncertainty Visualization is Error Prone. Proc. BELIV 2016 Download PDF
Qu, Z. and Hullman, J. Evaluating Visualization Sets: Trade-offs Between Local Effectiveness and Global Consistency. Proc. BELIV 2016 Download PDF
Author order denotes contribution