March 25, 2008
Interschool Lab Room 750 CEPSR
Speaker: Prof. Daniel Lee, University of Pennsylvania
How do animals process the tremendous amount of information coming from their senses, in time to take appropriate actions with their muscles? This type of robust sensorimotor processing is still difficult to replicate in robots even with the latest computers, sensors, and actuators. However, new advances in machine learning that borrow techniques from statistical physics, information theory, and differential geometry are helping to create new algorithms that replicate behaviors that animals routinely perform. I will describe some of my lab's recent work on artificial sensorimotor processing systems and demonstrate some of their latest feats and tricks.
Daniel D. Lee is currently Graduate Chair, Raymond S. Markowitz Faculty Fellow, and Associate Professor of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.A. in Physics from Harvard University in 1990, and his Ph.D. in Condensed Matter Physics from the Massachusetts Institute of Technology in 1995. Before coming to Penn, he was a researcher at Bell Laboratories, Lucent Technologies, from 1995-2001 in the Theoretical Physics and Biological Computation departments. He has received the NSF Career award and the Univ. of Pennsylvania Lindback award for distinguished teaching; he is a fellow of the Hebrew University Institute of Advanced Studies in Jerusalem, and a foreign affiliate of the Korea Advanced Institute of Science and Technology, and has helped organize the US-Japan National Academy of Engineering Frontiers of Engineering symposium. His research focuses on understanding the general principles that biological systems use to process and organize information, and on applying that knowledge to build better artificial sensorimotor systems. He resides in Leonia, New Jersey, with his wife Lisa, six-year old son Jordan, and four-year old daughter Jessica.