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David Banks

Photograph of David Banks
David Banks
Professor of the Practice of Statistics
Duke University
204 Caldwell Hall

Deming and the Industries of Today


Dr. Deming was one of the foundational leaders in industrial statistics, with contributions to experimental design, sampling, and process control. More importantly, he changed the culture of business leadership in two nations, and implicitly, around the world. But the industries of his day focused on manufacturing, while today’s industries reflect the knowledge economy. This talk asks the industrial statistics community to consider how to update and apply Dr. Deming’s ideas in the Big Data era. There are some very direct correspondences.


Dr. David Banks obtained a Ph.D. in Statistics in 1984. He won an NSF Postdoctoral Research Fellowship in the Mathematical Sciences, which he took at Berkeley. In 1986 he was a visiting assistant lecturer at the University of Cambridge, and then joined the Department of Statistics at Carnegie Mellon in 1987. In 1997 he went to the National Institute of Standards and Technology, then served as chief statistician of the U.S. Department of Transportation, and finally joined the U.S. Food and Drug Administration in 2002. In 2003, he returned to academics at Duke University.

He was the coordinating editor of the Journal of the American Statistical Association. He co-founded the journal Statistics and Public Policy and served as its editor. He is past director of the Statistical and Applied Mathematical Sciences Institute, past-president of the Classification Society and of the International Society for Business and Industrial Statistics, and has twice served on the Board of Directors of the American Statistical Association. He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. His research areas include computational advertising, dynamic text networks, adversarial risk analysis, human rights statistics, agent-based models, and certain topics in high-dimensional data analysis.

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