Microsoft Research - Distinguished Scientist and Co-Director
I will present on directions with harnessing predictive models to guide decision making. I will first discuss methods for using machine learning to ideally couple human and computational effort, focusing on several illustrative efforts, including spoken dialog systems and citizen science. Then I will turn to challenges with healthcare and describe work to field statistical models in real-world clinical settings, focusing on the opportunity to join predictions about outcomes with utility models to guide intervention. Finally, I will discuss work to leverage large-scale logs of behavioral data as sensor networks for public health, including recent efforts in pharmacovigilance.
Eric Horvitz is a distinguished scientist at Microsoft Research, where he co-directs the Microsoft Research-Redmond lab. His interests span theoretical and practical challenges with developing systems that perceive, learn, and reason, with a focus on inference and decision making under uncertainty. He has been elected a fellow of AAAI, the American Academy of Arts and Sciences, and the National Academy of Engineering, and was recently inducted into the CHI Academy. He received PhD and MD degrees at Stanford University. Information on publications, collaborations, and activities can be found at http://research.microsoft.com/~horvitz.