Robert E. (Rob) Kass is the Maurice Falk Professor of Statistics and Computational Neuroscience at Carnegie Mellon University. Rob received his PhD 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, since 2000, in the application of statistics to neuroscience. Rob Kass is known for his methodological contributions, and for several major review articles, including one with Adrian Raftery on Bayes factors (JASA, 1995), one with Larry Wasserman on prior distributions (JASA, 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 also written widely-read articles on statistical education. Recently, he and several co-authors published “Ten Simple Rules for Effective Statistical Practice” (PLOS Computational Biology, 2016).
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 of Statistical Science. He is an elected Fellow of IMS, ASA and AAAS. 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 ASA for his 2011 paper in the Annals of Applied Statistics with Ryan Kelly and Wei-Liem Loh. In 1991 he began the series of eight international workshops, Case Studies in Bayesian Statistics, which were 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 Neural Data, which began in 2002; the eighth iteration takes place in May, 2017. In 2014 Kass chaired an ASA working group that produced the forward-looking report Statistical Research and Training Under the BRAIN Initiative.
Kass has been on the faculty of the Department of Statistics at Carnegie Mellon since 1981; he joined the Center for the Neural Basis of Cognition (CNBC, run jointly by CMU and the University of Pittsburgh) in 1997, and the Machine Learning Department (in the School of Computer Science) in 2007. He served as Department Head of Statistics from 1995 to 2004 and was appointed Interim CMU-side Director of the CNBC in 2015.
COPSS R.A. Fisher Lecture
JSM 2017 in Baltimore, MD, USA, Wednesday, August 2, 4:00pm
The Importance of Statistics: Lessons from the Brain Sciences
The brain’s complexity is daunting, but much has been learned about its structure and function, and it continues to fascinate: on the one hand, we are all aware that our brains define us; on the other hand, it is appealing to regard the brain as an information processor, which opens avenues of computational investigation.
While statistical models have played major roles in conceptualizing brain function for more than 50 years, statistical thinking in the analysis of neural data has developed much more slowly. This seems ironic, especially because computational neuroscientists can—and often do—apply sophisticated data analytic methods to attack novel problems. The difficulty is that in many situations, trained statisticians proceed differently than those without formal training in statistics.
What makes the statistical approach different, and important? I will give you my answer to this question, and will go on to discuss a major statistical challenge, one that could absorb dozens of research-level statisticians in the years to come.