Data MASTER Colloquium

Data MASTER colloquium

Speaker:  Robert E. Kass, Carnegie Mellon University

Title: Statistical Thinking in Neuroscience

http://math.columbian.gwu.edu/data-master-seminar

Abstract:
Experimenters are typically adept at applying standard statistical techniques, while computational neuroscientists are capable of formulating mathematically sophisticated data analytic methods to attack novel problems in data analysis. Yet, in many situations, statisticians proceed differently than those without formal training in statistics. What is different about the the way statisticians approach problems? I will give you my thoughts on this subject, and will illustrate with  analyses drawn from my own work, involving neural spike trains and neuroimaging. I will also touch on the notion of scientific reproducibility, and will comment on potential roles for Bayesian inference.

Bio:
Robert E. (Rob) Kass received his Ph.D. in Statistics from the University of Chicago in 1980. His early work formed the basis for his book Geometrical Foundations of Asymptotic Inference, co-authored with Paul Vos. His subsequent research has been in Bayesian inference and, beginning in 2000, in the application of statistics to neuroscience. Kass is known not only for his methodological contributions, but also for several major review articles, including one with Adrian Raftery on Bayes factors (Journal of American Statistical Association, 1995) one with Larry Wasserman on prior distributions (Journal of American Statistical Association, 1996), and a pair with Emery Brown on statistics in neuroscience (Nature Neuroscience, 2004, also with Partha Mitra; Journal of Neurophysiology, 2005, also with Valerie Ventura). His book Analysis of Neural Data, with Emery Brown and Uri Eden, was published in 2014.

Kass has served as Chair of the Section for Bayesian Statistical Science of the American Statistical Association, 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 has been recognized by the Institute for Scientific Information as one of the 10 most highly cited researchers, 1995-2005, in the category of mathematics (ranked #4). In 2013 he received the Outstanding Statistical Application Award from the American StatisticalAssociation for his 2011 paper in the Annals of Applied Statistics with Ryan Kelly and Wei-Liem Loh. In 1991 he began the series of eightinternational workshops Case Studies in Bayesian Statistics, whichwere held every two years at Carnegie Mellon, and was co-editor of the six proceedings volumes that were published by Springer. He also founded and has co-organized the international workshop series Statistical Analysis of Neuronal Data, which began in 2002; the seventh iteration will occur in May, 2015.

Kass has been been on the faculty of the Department of Statistics at Carnegie Mellon since 1981 and served as Department Head from 1995 to2004; he joined the Center for the Neural Basis of Cognition in 1997,and the Machine Learning Department (in the School of Computer Science) in 2007.