David

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Personal Information

Name
David L. Waltz Ph.D.
Position
Director, CCLS
Email address

waltzatccls [dot] columbia [dot] edu

Business Address

Center for Computational Learning Systems
Columbia University
475 Riverside Drive MC 7717
New York, NY 10115

Phone
212-870-1275
Fax
212-870-1285
Personal Website
http://www.cs.columbia.edu/~waltz/
Biography

David L. Waltz has been Director of the Center for Computational Learning Systems (CCLS) at Columbia University since 2003. He was formerly President of the NEC Research Institute in Princeton, and from 1984-1993 was Director of Advanced Information Systems at Thinking Machines Corporation and Professor of Computer Science at Brandeis University. He had also been Professor of Electrical and Computer Engineering at the University of Illinois for 11 years. Waltz served as president of AAAI (American Association for Artificial Intelligence) from 1997-1999, and is a Fellow of ACM (Association for Computing Machinery) and AAAI, a Senior Member of IEEE (Institute for Electrical and Electronics Engineers), and former Chairman of ACM SIGART (Special Interest Group on Artificial Intelligence). He is currently on the Army Research Lab Technical Advisory Board and the Advisory Board of the Florida Institute for Human and Machine Cognition, and has served on recent external advisory boards for Rutgers University, Carnegie-Mellon University, Brown University, and EPFL (Ecole Polytechnique Federale de Lausanne). He is on the Editorial Advisory Board for IEEE Intelligent Systems, and has served on the Computing Research Association Board, and NSF Computer Science Advisory Board.

Dr. Waltz received all his degrees from MIT, including his Ph.D. for work at the MIT AI Lab. His thesis on computer vision originated the field of constraint propagation, and with Craig Stanfill, he originated the field of memory-based reasoning. His current primary research interest is in machine learning applications, especially to the electric power grid. His research interests have also included massively parallel information retrieval, data mining, learning and automatic classification with applications protein structure prediction, and natural language processing.

History

Member for
7 years 31 weeks
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