Daniel D. Lee, Ph.D.
Director, GRASP (General Robotics Automation, Sensing, Perception) Lab
Raymond S Markowitz Faculty Fellow
Evan C Thompson Term Chair for Excellence in Teaching
Dept. of Electrical and Systems Engineering
Dept. of Computer and Information Science (Secondary)
Dept. of Bioengineering (Secondary)
University of Pennsylvania
200 S. 33rd Street
Philadelphia, PA 19104
Daniel Lee is the Evan C Thompson Term Chair, Raymond S. Markowitz Faculty Fellow, and Professor in the School of Engineering and Applied Science at the University of Pennsylvania. He received his B.A. summa cum laude 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 AT&T and Lucent Bell Laboratories in the Theoretical Physics and Biological Computation departments. He is a Fellow of the IEEE 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 organized the US-Japan National Academy of Engineering Frontiers of Engineering symposium. As director of the GRASP Laboratory and co-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.
It is ironic that computers excel at logical reasoning that take humans many years of specialized training and education to learn, yet machines are still unable to perform simple everyday tasks that we take for granted. My groupÕs research focuses on learning representations that enable autonomous systems to efficiently reason about real-time behaviors in an uncertain world. In particular, much of our work has used low-dimensional representations to overcome the curse of dimensionality in perception, planning and control tasks. We use machine learning algorithms and computational neuroscience models, in addition to implementations on a variety of robotic platforms to study how to build better sensorimotor systems that can adapt and learn from experience.
You can find an updated list of my publications on Google Scholar.
My wife, Lisa Park, is an ophthalmologist at NYU Medical School. We live with our son, Jordan, and daughter, Jessica, in Leonia, New Jersey.