MACHINE LEARNING IN AUTONOMOUS SYSTEMS: THEORY AND PRACTICE
Sponsored by the Philadelphia Chapter of the IEEE Aerospace and Electronic Systems Society
Current Artificial Intelligence systems for perception and action incorporate a number of techniques: optimal observer models, Bayesian filtering, probabilistic mapping, trajectory planning, dynamic navigation and feedback control. In order to model data variability due to pose, illumination, and background changes, low-dimensional manifold representations have long been used in machine learning. But how well can such manifolds be processed by neural networks? I will briefly describe and demonstrate some of these methods for autonomous driving and for legged and flying robots, and contrast these models with neural representations and computation
Date and Time
Location
Hosts
Registration
- Date: 11 Apr 2018
- Time: 12:00 PM to 01:00 PM
- All times are (GMT-05:00) US/Eastern
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- 1947 N 12th St.
- Philadelphia, Pennsylvania
- United States 19122
- Building: Engineering Building
- Room Number: Room 301, Dean's Seminar Room
- Starts 20 March 2018 01:00 PM
- Ends 09 April 2018 05:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Prof. Daniel Lee of University of Pennsulvania
Biography:
Dr. Daniel Lee is the UPS Foundation Chair Professor in the School of Engineering and Applied Science at the University of Pennsylvania. He received his B.A. summa cum laude from Harvard University and his Ph.D. in Condensed Matter Physics from the Massachusetts Institute of Technology. Before coming to Penn, he was a researcher at AT&T and Lucent Bell Laboratories in the Theoretical Physics and Biological Computation departments. He is a Fellow of the IEEE and AAAI and has received the National Science Foundation CAREER award and the University of Pennsylvania Lindback award for distinguished teaching. He was also a fellow of the Hebrew University Institute of Advanced Studies in Jerusalem, an affiliate of the Korea Advanced Institute of Science and Technology, and has organized the US-Japan National Academy of Engineering Frontiers of Engineering symposium and the Neural Information Processing Systems (NIPS) conference. As director of the GRASP Laboratory and founding director of the CMU-Penn University Transportation Center, his group focuses on understanding general computational principles in biological systems, and on applying that knowledge to build autonomous systems.