matkay
(734) 763-2285
Applications: Health, Personal Informatics, Real-time Transit Prediction Methodologies: Bayesian Data Analysis, Human-Computer Interaction, Information Visualization Relevant Projects: Health Data Exploration Project, Google

Matthew Kay

Assistant Professor, School of Information

 

My research includes work on communicating uncertainty, usable statistics, and personal informatics. People are increasingly exposed to sensing and prediction in their daily lives (“how many steps did I take today?”, “how long until my bus shows up?”, “how much do I weigh?”). Uncertainty is both inherent to these systems and usually poorly communicated. To build understandable data presentations, we must study how people interpret their data and what goals they have for it, which informs the way that we should communicate results from our models, which in turn determines what models we must use in the first place. I tackle these problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building and evaluating interactive systems, and designing and testing visualization techniques. My work draws on approaches from human-computer interaction, information visualization, and statistics to build information visualizations that people can more easily understand along with the models to back those visualizations.