Understanding Human Dynamics using Data Science
Department of Computer Science Seminar: Understanding Human Dynamics using Data Science, Kevin S. Xu, Ph.D.
Friday, December 2, 2016, 3:30 p.m., 117 Hayes Hall
Recent advances in sensing technology have enabled us to study the dynamics of human behavior on a much larger scale with finer granularity, from the advent of the World Wide Web and on-line social networks to the mass adoption of smartphones and the introduction of wearable technology. Many people now carry sensors capable of continuously collecting data on their movements, activities, interactions, and even their physical responses to external stimuli. While it is incredible that we can collect and store such rich data, there is a need to develop robust machine learning and signal processing algorithms to perform data analytics on these new modalities of data, which are quite different from traditionally studied data types such as images and text. This talk will introduce several application settings involving human dynamics data and present principled algorithms for analyzing such data. These applications range from predicting patterns of social interactions between people in on-line social networks to identifying patterns of activation of a person’s autonomic nervous system using wearable sensors.
Kevin S. Xu received the B.A.Sc. degree in Electrical Engineering from the University of Waterloo in 2007 and the M.S.E. and Ph.D. degrees in Electrical Engineering: Systems from the University of Michigan in 2009 and 2012, respectively. He was a recipient of the Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Master’s and Doctorate Scholarships. He is currently an assistant professor in the EECS Department at the University of Toledo and has previously held industry research positions at Technicolor and 3M. His main research interests are in machine learning and statistical signal processing with applications to network science and human dynamics.
VenueBGSU Hayes Hall
Hayes Hall 117