Daniel D. Lee, Ph.D.
Professor
Director, GRASP (General Robotics Automation,
Sensing, Perception) Lab
UPS Foundation Chair
Dept. of Electrical and
Systems Engineering
Dept. of Computer and Information Science (Secondary)
Dept. of Bioengineering (Secondary)
University of Pennsylvania
460
Levine
200 S. 33rd Street
Philadelphia, PA 19104
Office: 215-898-8112
Fax: 215-573-2068
Email: ddlee@seas.upenn.edu
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 in Physics from
Harvard University 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 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 organized the US-Japan
National Academy of Engineering Frontiers of Engineering symposium. As director of the GRASP Laboratory and founding 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 practicing
in New York City. We live with our
son, Jordan, and daughter, Jessica, in Leonia, New Jersey.