Many important public health questions are difficult and costly to answer. What kind of risks do highly localized sources of pollution, like dry cleaners that use volatile chemicals, pose to the health of nearby residents? Are people with many friends healthier, or do those friendships increase the likelihood of infectious disease? Do frequent visits to public spaces like bars, gyms and restaurants affect a person's health? Researchers have been striving for generations to answer such questions, using health surveys of samples of individuals and computational studies of simulated populations. Now, however, the rise of social media and the burgeoning field of data science provide powerful tools to find high-precision, real-world answers with little cost or effort. This talk provides an overview of the TwitterHealth project at the University of Rochester, a joint effort of our Department of Computer Science and School of Medicine and Dentistry.
Bio: Henry Kautz is Director of the Institute for Data Science and Chair of the Department of Computer Science at the University of Rochester. He has served as President of the Association for the Advancement of Artificial Intelligence, and is a Fellow of AAAS, ACM, and AAAI.