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