V.G.Vinod Vydiswaran

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V.G.Vinod Vydiswaran, PhD, is Assistant Professor in the Department of Learning Health Sciences with a secondary appointment in the School of Information at the University of Michigan, Ann Arbor.

Dr. Vydiswaran’s research focuses on developing and applying text mining, natural language processing, and machine learning methodologies for extracting relevant information from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets). His previous work includes developing novel information retrieval models to assist clinical decision making, modeling information trustworthiness, and addressing the vocabulary gap between health professionals and  laypersons.

Matthew Kay

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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.

 

Michael O’Donnell

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We study the link between health behavior, conditions, culture, medical costs and productivity, based on data from more than 2 million people. We have developed tools to predict future diseases and medical costs.  We use a wide range of analysis techniques.