Login
Computer Science @ Rochester
Friday, March 24, 2006
11:00 AM
CSB 209
Henry Kautz
U. Washington
Understanding Human Behavior from Sensor Data
The convergence of advances in algorithms for probabilistic reasoning and the development of low-cost, easily-deployed sensors is reviving the dream of AI to develop systems that can understand the narrative of ordinary human life. representations and hierarchical models of goals, plans, and actions.echnologies such as RFID tags, GPS, motes, and wearable multi-modal sensors allow us to gather direct information about many aspects of human experience.

I will describe recent work with my students and colleagues on developing systems that learn patterns of human activity for everyday tasks, both indoors and outdoors, using a variety of dynamic probabilistic models.Bns of these techniques to healthcare systems as part of the Assisted Cognition Project, a joint effort between our departments of computer science and rehabilitation medicine.

Bio: Henry Kautz is a Professor in the Department of Computer Science and Engineering at the University of Washington. He joined the faculty in the summer of the year 2000 after a career at Bell Labs and AT&T Laboratories, where he was Head of the AI Principles Research Department. His academic degrees include an A.B. in mathematics from Cornell University, an M.A. in Creative Writing from the Johns Hopkins University, and M.Sc. in Computer Science from the University of Toronto, and a Ph.D. in computer science from the University of Rochester. He is a recipient of the Computers and Thought Award from the International Joint Conference on Artificial Intelligence and a Fellow of the American Association for Artificial Intelligence.