Stephen E. Fienberg
Carnegie Mellon University
Election Year: 1999
Primary Section: 32, Applied Mathematical Sciences
Secondary Section: 53, Social and Political Sciences
Membership Type: Member
Stephen E. Fienberg is Maurice Falk University Professor of Statistics and Social Science at Carnegie Mellon University, and co-director of the Living Analytics Research Centre (jointly operated by Carnegie Mellon and Singapore Management University), with appointments in the Department of Statistics, the Machine Learning Department, and the Heinz College. He is a statistician recognized for his contributions to statistical methodology, especially linked to the analysis of categorical data.
Dr. Fienberg was born and raised in Toronto Canada and received his Ph.D. in Statistics from Harvard University in 1968. He has been on the faculties of the University of Chicago, the University of Minnesota, and York University, where he served as Vice President Academic. He joined the faculty of Carnegie Mellon in 1980. He has served as President of the Institute of Mathematical Statistics and the International Society of Bayesian Analysis, and has been the editor of several major statistical journals.
My principal research interests lie in the development of statistical methodology and its application in a number of different domains. I have worked on the general statistical theory of log-linear models for categorical data and its application to problems representable in the form of multi-dimensional contingency tables and in the form of networks. I have worked on statistical methods for large-scale sample surveys such as those carried out by the federal government, including the study of non-sampling errors, the use of surveys to adjust census results for differential undercount, cognitive aspects of the design of survey questionnaires, and formal parallels in the design and analysis of sample surveys and randomized experiments. My book with Margo Anderson, Who Counts?, chronicles the story of the 1990 decennial census and efforts to use sample to adjust census results for differential undercount. I currently work on Bayesian mixed membership models and their application, and a number of different aspects of disclosure limitation for statistical databases. I have linked this privacy protection research to my long-standing interest in categorical data methods, network analysis, and census-like data.