Curtis McDonald

Curtis is a postdoctoral scholar at the Simons Institute Machine Learning Pod. His research interests include Bayesian methods for machine learning, diffusion models, sampling algorithms, and stochastic control theory.

Prior to joining the research team at Berkeley, Curtis completed his PhD in Statistics and Data Science at Yale University in 2025 advised by Andrew Barron. He received a Master's of Applied Science in Applied Mathematics and Engineering from Queen's University in 2019, advised by Serdar Yuksel. He also completed his Bachelor's of Applied Science in Applied Mathematics and Engineering in 2017 at Queen's University.

In 2025 Curtis was awarded the Francis J. Anscombe award by the Statistics and Data Science Department of Yale University for outstanding academic performance in the graduating class.