David L. Banks

Banks

Professor of the Practice of Statistical Science

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 has chaired that section, 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; 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 74 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 is currently the president of the International Society for Business and Industrial Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He recently 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.

Appointments and Affiliations

  • Professor of the Practice of Statistical Science
  • Director of the Statistical and Applied Mathematical Sciences Institute

Contact Information

  • Office Location: 210A Old Chemistry Building, Duke University, Durham, NC 27708
  • Office Phone: (919) 423-0792
  • Email Address: david.banks@duke.edu
  • Websites:

Education

  • Ph.D. Virginia Polytech Institute and State University, 1984
  • M.S. Virginia Polytech Institute and State University, 1980
  • B.A. University of Virginia, 1977

Courses Taught

  • FOCUS 195FS: Special Topics in Focus
  • MATH 790-71: Current Research in Applied Mathematics
  • STA 101L: Data Analysis and Statistical Inference
  • STA 110FS: Focus Program - Introductory Special Topics in Statistics
  • STA 393: Research Independent Study
  • STA 493: Research Independent Study
  • STA 521L: Predictive Modeling and Statistical Learning
  • STA 790-1: Special Topics in Statistics
  • STA 790: Special Topics in Statistics
  • STA 993: Independent Study

In the News

Representative Publications

  • Babu, GJ; Banks, D; Cho, H; Han, D; Sang, H; Wang, S, A Statistician Teaches Deep Learning, Journal of Statistical Theory and Practice, vol 15 no. 2 (2021) [10.1007/s42519-021-00193-0] [abs].
  • Banks, D; Cron, A; Raskind, A, Bayesian metrology in metabolomics, Chemometrics and Intelligent Laboratory Systems, vol 208 (2021) [10.1016/j.chemolab.2020.104208] [abs].
  • Andrews, E; Eierud, C; Banks, D; Harshbarger, T; Michael, A; Rammell, C, Effects of Lifelong Musicianship on White Matter Integrity and Cognitive Brain Reserve., Brain Sciences, vol 11 no. 1 (2021) [10.3390/brainsci11010067] [abs].
  • Banks, DL; Hooten, MB, Statistical Challenges in Agent-Based Modeling, The American Statistician, vol 75 no. 3 (2021), pp. 235-242 [10.1080/00031305.2021.1900914] [abs].
  • Insua, DR; Banks, D; Ríos, J; González-Ortega, J, Adversarial Risk Analysis as a Decomposition Method for Structured Expert Judgement Modelling, vol 293 (2021), pp. 179-196 [10.1007/978-3-030-46474-5_7] [abs].