James Scott
James G. Scott
Professor and Chair, Department of Statistics and Data Sciences
The University of Texas at Austin
My work spans statistics, machine learning, AI, computation, and applied data science. I research, teach, write, and consult on how to draw reliable conclusions from complex data.
Research
My research focuses on Bayesian inference, statistical machine learning, statistical computing, and interdisciplinary applications. I have worked on shrinkage priors, sparse inference, multiple testing, Bayesian computation, and the statistical reliability of machine-learning methods.
Books
I am coauthor of AIQ: How People and Machines Are Smarter Together and author of Data Science in R: A Gentle Introduction, a free textbook used in introductory data-science courses at UT and elsewhere.
Teaching
I teach statistical modeling, data science, probability, inference, and machine learning to students in statistics, data science, business, economics, and related fields.
Consulting and expert work
I consult on statistical, data-scientific, and machine-learning questions in litigation and other high-stakes settings, with particular attention to uncertainty, causal claims, sampling, model validity, and empirical evidence.
Contact
Email: james.scott@austin.utexas.edu
Department of Statistics and Data Sciences, University of Texas at Austin