unnamed-1David S. Matteson is Associate Professor of Statistical Science and Social Statistics at Cornell University, where he is a member of the ILR School, Computing and Information Science, the Center for Applied Mathematics, the Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering courses. Professor Matteson received his PhD in Statistics at the University of Chicago (2008) and his BSB in Finance, Mathematics, and Statistics at the University of Minnesota (2003). He has received a CAREER Award from the National Science Foundation (2015), a Faculty Research Award from the Xerox/PARC Foundation (2014), and Best Paper Awards from the annual R/Finance conference (2011, 2013) and the National Association of Rehabilitation Research (2015). He is an Associate Editor of the Journal of the American Statistical Association-Theory and Methods, Biometrics, and Statistica Sinica. He is an Officer for the Business and Economic Statistics Section of the American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society. He is coauthor of Statistics and Data Analysis for Financial Engineering (2nd ed., Springer, 2015).


I am particularly interested in high dimensional data analysis, which has become one of the most important areas of statistical research, with applications in many fields. While many recent efforts have attempted to incorporate mathematical and contextual constraints into analysis, most work on these difficult problems has focused on independent observations. I have found dependence to be a valuable source of additional information, not merely an additional complexity, and I endeavor to make major contributions to this exciting new field.

As a data scientist, my primary research focus has involved the analysis of complex big data and the development of accompanying statistical methodology. This includes data structured by time indices or spatial locations, as well as functional and network structured data. My research has been heightened by biological, environmental, financial, operational and sociological applications. My collaborations with scientists and industry professionals continue to be mutually rewarding and personally enjoyable. My most up to date preprint manuscripts are available here: arXiv.orgMy research interests include:

    • Applied Biophysics
    • Bayesian Analysis
    • Biostatistics
    • Dimension Reduction
    • Disability
    • Emergency Medical Services
    • Financial Econometrics
    • Functional Data Analysis
    • Machine Learning
  • Multivariate Statistics
  • Neuroscience
  • Nonparametrics
  • Point Processes
  • Semiparametrics
  • Signal Processing
  • Spatio-Temporal Modeling
  • Sustainable Energy
  • Time Series

Ph.D. in Statistics, 2008
University of Chicago
Dissertation: Statistical Inference for Nonlinear Multivariate Time Series
Advisor: Ruey S. Tsay, H.G.B. Alexander Professor of Econometrics and Statistics

B.S.B., 2003
Majors: Mathematics, Statistics, Finance; Minor: Economics
University of Minnesota