Scholarly Chairs

 Penn Engineering is again delighted to announce the assignment of scholarly chairs to two of its professors. The appointment to an endowed professorship is the highest honor that the University can award to a member of its faculty. Holders of a scholarly chair must have distinguished records of teaching and research and are expected to serve as inspirations to their students and as models to their junior colleagues. Each professorship is supported by a permanent endowment. Appointments are made for tenyear renewable terms.

Daniel E. Koditschek has been named the Alfred Fitler Moore Professor of Electrical and Systems Engineering. He joined the faculty of Penn in January, 2005 and assumed the post of Chair of the Electrical and Systems Engineering Department within the School of Engineering and Applied Science. Dr. Koditschek received his bachelor’s degree in Engineering and Applied Science and his M.S. and Ph.D. degrees in Electrical Engineering in 1981 and 1983—all from Yale University. He served on the Yale faculty in Electrical Engineering until moving to the University of Michigan a decade later.

Dr. Koditschek’s research interests include robotics and, more generally, the application of dynamical systems theory to intelligent mechanisms. His archival journal and refereed conference publications, numbering well over 100, have appeared in a broad spectrum of venues ranging from the Transactions of the American Mathematical Society through the Journal of Experimental Biology, with a concentration in several of the IEEE publications and related Transactions. Various aspects of this work have received mention in general scientific publications such as Scientific American and Science as well as in the popular and general lay press such as The New York Times and Discover Magazine. Koditschek is a member of the AAAS, AMS, ACM, MAA, SIAM, SICB and Sigma Xi and is a Fellow of the IEEE.

Dr. Koditschek holds secondary appointments within the School of Engineering and Applied Science in the departments of Computer and Information Science as well as Mechanical Engineering and Applied Mechanics.

This Chair is one of three scholarly chairs established as part of The Moore School Trust with the purpose of perpetuating the Moore family name.

Michael Kearns has been named the National Center Professor in Resource Management and Technology. Dr. Kearns received his Ph.D. in Computer Science from Harvard University in 1989, where his dissertation, “The Computational Complexity of Machine Learning,” won a Distinguished Dissertation Award from the Association for Computing Machinery and was published by the MIT Press. Following postdoctoral fellowships at MIT and the University of California at Berkeley, he spent a decade in basic research at Bell Laboratories and AT&T Laboratories, where he headed the Artificial Intelligence and Machine Learning research department.

He joined the Penn faculty in Computer and Information Science in 2002. He is co-director of Penn’s Institute for Research in Cognitive Science, and holds a secondary appointment in the Operations and Information Management department of the Wharton School.

Dr. Kearns’ research interests lie in artificial intelligence, machine learning, and related areas. In recent years he has been particularly active in research at the intersection of computer science, economics, and game theory, as well as in topics in computational finance. He has recently designed the new undergraduate course, “Networked Life,” open to all majors and levels at Penn, which examines a rich mixture of topics in social network theory, economics, mathematics, and computer science. He is co-author with U.V. Vazirani of the book, An Introduction to Computational Learning Theory, published by the MIT Press in 1994.

Dr. Kearns is a Fellow of the American Association for Artificial Intelligence (AAAI), has served as program chair for many of the major international conferences on AI and machine learning, and has served on the editorial boards of the Journal of the ACM, SIAM Journal on Computing, Mathematics of Operations Research, Machine Learning, and many other journals.