David Banks obtained an M.S. in Applied Mathematics from Virginia Tech
in 1982, followed by 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.
David Banks 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 co-founded the American Statistical Association's Section on National Defense and Homeland Security, and its Section on Text Analysis, and has chaired those sections, as well as the sections on Risk Analysis and on Statistical Learning and Data Mining. In 2003 he led a research program on Data Mining at the Statistical and Applied Mathematical Sciences Institute (SAMSI); in 2008, he led a research program at the Isaac Newton Institute on Theory and Methods for Complex, High-Dimensional Data; in 2012, he led another SAMSI research program, on Computational Advertising. He has published more than 100 refereed articles, edited eight books, and written four monographs.
David Banks is past-president of the Classification Society, and has twice served on the Board of Directors of the American Statistical Association. He was the president of the International Society for Business and Industrial Statistics. He is a fellow of the American Statistical Association , the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He was the director of SAMSI from 2018-2022. In 2015 he won the American Statistical Association's Founders Award.
His research areas include models for dynamic networks, dynamic text networks, adversarial risk analysis (i.e., Bayesian behavioral game theory), human rights statistics, agent-based models, forensics, and certain topics in high-dimensional data analysis.