Ray Bai

Ray Bai is currently an assistant professor of Statistics at George Mason University. His current research focuses mainly on Bayesian statistics, deep learning, and causal inference. He is especially interested in developing new methodologies and scalable algorithms for high-dimensional data where the number of parameters and/or the sample size is large. His work has been published in leading journals such as Journal of the American Statistical Association, Journal of Machine Learning Research, Journal of Computational and Graphical Statistics, and Statistica Sinica.

Before joining George Mason in 2025, Bai was an assistant professor at the University of South Carolina, and before that, he was a postdoc at the University of Pennsylvania. He earned his PhD in Statistics from the University of Florida, his MS in Applied Mathematics from the University of Massachusetts Amherst, and his BA from Cornell University. Prior to his academic career, he worked in industry for five years as an engineer and as a financial software analyst.

Research Interests

• Bayesian inference

• Deep learning

• Deep generative models

• High-dimensional statistics

• Scalable algorithms

• Causal inference

• Survival analysis

Degrees

• PhD, Statistics, University of Florida, 2018

• MS, Applied Mathematics, University of Massachusetts Amherst, 2012

• BA, Economics and Government, Cornell University, 2007