David Waltz's Homepage

David L. Waltz

David Waltz

Director, Center for Computational Learning Systems, Columbia University







Office: 212-870-1275
FAX: 212-870-1285
EMAIL: my last name at ccls.columbia.edu
WWW: http://www1.cs.columbia.edu/~waltz/
SNAIL: Suite 850, 475 Riverside Drive, MC 7717, New York, NY, 10115
Department Administrator: Nancy Burroughs, 212-870-1280, nancy at ccls.columbia.edu


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 (CSL and ECE Department) for 11 years. Waltz served as president of AAAI (American Association for Artificial Intelligence) from 1997-1999, and is a Fellow of AAAI and ACM (Association for Computing Machinery), 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, the Technical Advisory Board of 4C (Cork Constraint Computation Center, Ireland) 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 Advisory Board for IEEE Intelligent Systems, and the Computing Community Consortium Board of the CRA (Computing Research Association), 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 branch of CBR (Case-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.