Faculty - Dr. Nathan Ratliff
PhD - Carnegie Mellon University
Research Assistant Professor
Early in his post secondary education, Dr. Ratliff found an interest in robotics and machine learning studying under Dr. Dieter Fox at the University of Washington. After receiving a B.S. is mathematics and a B.S. is computer engineering in 2003, he spend a short year as a software engineer at Amazon before embarking on his graduate studies in the Robotics at Carnegie Mellon University under Dr. J. Andrew Bagnell in the fall of 2004. Dr. Ratliff received his Ph.D. in Robotics from Carnegie Mellon in 2009. Presently, he spends his summers in Seattle collaborating strongly with the University of Washington and the Seattle-based Intel Research lab.
Dr. Ratliff's research interests span robotics and machine learning and include the specialized areas of imitation learning, structured prediction, kernel methods, convex optimization, mobile manipulation, navigational planning, LADAR segmentation, optical character recognition, grasp planning, and quadrupedal locomotion. He is particularly interested in driving novel research in machine learning using difficult problems in robotics. His thesis work develops a rigorous framework formalizing inverse optimal control for imitation learning. Tools derived within this framework allow roboticists to efficiently train existing state-of-the-art planners to embody and generalize demonstrated behavior to novel domains.
Dr. Ratliff also has a personally maintained website which can be found at http://www.ttic.edu/ratliff
