Robert E. Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience in the Department of Statistics and Data Science, the Machine Learning Department, and the Neuroscience Institute at Carnegie Mellon University.
Kass's early research was on differential geometry in statistics,[2] which formed the basis for his book Geometrical Foundations of Asymptotic Inference[3] (with Paul Vos), and on Bayesian methods.
Kass's best-known work includes a comprehensive re-evaluation of Bayesian hypothesis testing and model selection,[4][5] and the selection of prior distributions,[6] the relationship of Bayes and Empirical Bayes methods,[7] Bayesian asymptotics,[8][9] the application of point process statistical models to neural spiking data,[10][11] the challenges of multiple spike train analysis,[12][13] the state-space approach to brain-computer interface,[14] and the brain's apparent ability to solve the credit assignment problem during brain-controlled robotic movement.
[15] Kass's book Analysis of Neural Data[16] (with Emery Brown and Uri Eden) was published in 2014.
At Carnegie Mellon University he was Department Head of Statistics from 1995 to 2004 and Interim Co-director of the joint CMU–University of Pittsburgh Center for the Neural Basis of Cognition 2015–2018.