About Me

I use ML and HPC to accelerate scientific discovery1 @ ALCF.

I’m generally interested in the application of machine learning to computational problems in science, particularly within the context of high performance computing.

My current research focuses on using deep generative modeling to help build better sampling algorithms in lattice gauge theory. In particular, I’m interested in building gauge equivariant neural network architectures and using inductive priors to incorporate physical symmetries into machine learning models.


Recent Talks

You can get a live view of some of my recent talks here

I received my PhD in Physics from the University of Iowa in 2019 and my thesis was on Learning Better Physics: A Machine Learning Approach to Lattice Gauge Theory. Prior to this, I completed two bachelors degrees (Engineering Physics and Applied Mathematics, 2015) from The University of Illinois at Urbana-Champaign. My undergraduate dissertation was titled Energy Storage in Quantum Resonators and was supervised by Professor Alfred Hübler within the Center for Complex Systems Research at UIUC.

As a member of the Data Science Group at ALCF, I work on:

Recent Work

Recent Talks

Active Projects


Assistant Computational Scientist ALCF 2022
Postdoc ALCF 2019 2022
Graduate Researcher ANL 2018 2019


PhD Physics University of Iowa 2019
B.Sc Physics UIUC 2015
B.Sc Math UIUC 2015



from datetime import date
print(f"Last updated: {date.today().strftime('%d %B %y')}")
Last updated: 14 November 23

Back to top


  1. Mostly getting supercomputers to stop yelling at each other .↩︎


BibTeX citation:
  author = {Foreman, Sam},
  title = {Sam {Foreman}},
  url = {https://samforeman.me},
  langid = {en}
For attribution, please cite this work as:
Foreman, Sam. n.d. “Sam Foreman.” https://samforeman.me.