Thanks for landing. This site has some information on my research and experience. To contact me directly, email me at jessica dot hullman at gmail or jhullman at umich dot edu.

I am currently studying HCI and information visualization at the University of Michigan School of Information with advisor Eytan Adar. I began my phd in 2009. I have a former M.F.A. (Prose) from the Jack Kerouac School for Disembodied Poetics at Naropa University, and an M.S.I. in Information Analysis & Retrieval from the University of Michigan School of Information.

My research focuses on how people understand data in online settings, particularly when the data is presented graphically. The popularity of information visualizations online by analysts and non-statiscians alike has the potential to generate useful collective insights into socially-relevant data. Yet the usefulness of the crowd signal can be threatened by risks to accurate visualization interpretation. These can be imposed by the graph design, individual differences in graph and statistical literacy, cultural differences, and social influences. I use my work to identify how systematic biases, both cognitive and perceptual, affect our ability to objectively analyze visual information. My goal is to identify innovative ways in which to improve non-expert analysis of data and graphs so as increase both individual and collective accuracy.

I conduct large online experiments, often using crowdsourcing platforms like Amazon's Mechanical Turk. Recent work has included studying social and cultural influences on visualization interpretation, the effects of various storytelling techniques on interpretation, and visual methods for improving individual and group understandings of the error associated with a trend.