Robert E. Kass

Biosketch

Robert E. (Rob) Kass is the Maurice Falk Professor of Statistics and Computational Neuroscience in the Department of Statistics & Data Science, the Machine Learning Department, and the Neuroscience Institute at Carnegie Mellon University. He received a B.A. in Mathematics from Antioch College, a Ph.D. in Statistics from the University of Chicago, and was a postdoctoral fellow at Princeton University before joining Carnegie Mellon in 1981. Kass was Department Head of Statistics for 9 years and Interim Director of the precursor to the Neuroscience Institute for 3 years. He served as Chair of the Statistics Section of the American Association for the Advancement of Science, founding Editor-in-Chief of the journal Bayesian Analysis, and Executive Editor (editor-in-chief) of the international review journal Statistical Science. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He received the Outstanding Statistical Application Award from the American Statistical Association and what is now called the Distinguished Achievement Award and Lectureship from the Committee of Presidents of Statistical Societies.

Research Interests

Kass's early work was on Bayesian inference, and on differential geometry in statistics; since 2000 his interest has focused on statistical methods in neuroscience. These different endeavors have been united by the goal of understanding how reasoning from data produces reliable scientific knowledge, because convincing paths toward progress require detailed statistical research. In the case of Bayesian inference, Kass and colleagues provided comprehensive re-assessment of two of the most fundamental issues, evaluation of evidence concerning hypotheses and determination of prior probabilities, both of which relied on new methods and re-formulations developed by Kass and many others. In neuroscience, Kass has concentrated mainly on analysis of data representing the primary mode of communication among neurons, known as spike trains, which are described well by mathematical models called point processes. His work has developed, investigated, and illustrated the utility of tractable data-analytic statistical models within the point process framework. Physiological application domains have included vision, audition, memory, movement, and brain-computer interfaces. The most recent work has focused on identifying interactions across two or more parts of the brain during behavioral tasks. Kass is co-author of the book Analysis of Neural Data, and has many overview articles that highlight the often-subtle ways statistical reasoning can advance science.