David Ruppert is Andrew Schulz Jr. Professor of Engineering, School of Operations Research and Information Engineering, and Professor of Statistical Science, Cornell University. He received a BA in Mathematics from Cornell University in 1970, an MA in Mathematics from the University of Vermont in 1973, and a PhD in Statistics and Probability from Michigan State University in 1977. He was Assistant and then Associate Professor of Statistics at the University of North Carolina, Chapel Hill, from 1977 to 1987. He is a Fellow of the ASA and IMS and received the Wilcoxon Prize in 1986. Professor Ruppert was named "Highly cited" researcher by ISIHighlyCited.com and was ranked 21st in mathematics by journal citations. He has had 29 PhD students, many of them now leading researchers. Professor Ruppert has worked on stochastic approximation, transformations and weighting in regression, and smoothing. His current research focuses on astrostatistics, neuroscience, measurement error models, splines, functional data analysis, semiparametric regression, and environmental statistics. He has published over 130 articles in refereed journals and has published five books, Transformation and Weighting in Regression, Measurement Error in Nonlinear Models (first and second editions), Semiparametric Regression, Statistics and Finance: An Introduction, and Statistics and Data Analysis for Financial Engineering (first and second edition).
Professor Ruppert's current research is on calibration and uncertainty analysis, semiparametric regression, splines in statistics, functional data analysis, astrostatistics, biostatistics, fMRI and ICA. He has had continuous external research funding since 1978 with grants from NSF, NIH, and EPA. He has published over 125 research papers.
In the past, Professor Ruppert has taught courses on regression, experimental design, measurement error, Taguchi methods, nonparametric estimation, time series, data mining, mathematical statistics, rank tests, statistical methods for risk analysis, and asymptotic theory. More recently, he has developed PhD courses on Bayesian statistics and functional data analysis and developed four courses for undergraduates and master's students: ORIE 3120, Industrial Data and Systems Analysis (developed and taught jointly with Peter Jackson and using their lecture notes); ORIE 4630, Operations Research Tools for Financial Engineering (which uses Professor Ruppert's textbook, Statistics and Finance: An Introduction); ORIE 5640, Statistics for Financial Engineering (which uses Professor Ruppert's textbook Statistics and Data Analysis for Financial Engineering); and STSCI 2150, Introductory Statistics for Biology.
- Shetty, R., J. Roman-Duval, S. Hony, D. Cormier, R S. Klessen, L K. Konstandin, T. Loredo, E W. Pellegrini, D Ruppert. 2016."Simultaneously modelling far-infrared dust emission and its relation to CO emission in star forming galaxies." Monthly Notices of the Royal Astronomical Society 460 (1): 67-81.
- Risk, B B., D S. Matteson, R N. Spreng, D Ruppert. 2016. "Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments." NeuroImage 142: 280-292.
- Kowal, D., D. Matteson, David Ruppert. 2016. "A Bayesian Functional Dynamic Linear Model." Journal of the American Statistical Association.
- Xiao, Luo., Y. Li, David Ruppert. 2013. "Fast Bivariate P-splines: the Sandwich Smoother." JRSS-B 75: 577-599.
- Ruppert, David, David S. Matteson. 2015. Statistics and Data Analysis for Financial Engineering, 2nd Edition.
Selected Awards and Honors
- Distinguished Alumni Award (Department of Statistical Science, Cornell University) 2014
- "Highly cited" researcher (Wilcoxon Award 1986 - ISIHighlyCited.com) 2010
- Ranked #21 by citations in mathematics (Essential Science Indicators) 2010
- Fellow of the Institute of Mathematical Statistics (1986) and American Statistical Association 1989
- Wilcoxon prize for best practical applications paper (Technometrics) 1986
MICHIGAN STATE UNIV 1977