Sam Foreman Sam Foreman is a computational scientist with a background in high energy physics at the ALCF.
He is generally interested in the application of machine learning to computational problems in physics, particularly within the context of high performance computing.
Sam’s current research focuses on using deep generative modeling to help build better sampling algorithms for simulations in lattice gauge theory.