About Me

I’m currently an Assistant Computational Scientist working in the data science group at the Leadership Computing Facility at Argonne National Laboratory.

I’m generally interested in the application of machine learning to computational problems in physics, 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.

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.

Recent Work

• M. Zvyagin, A. Brace, K. Hippe, et. al., GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics, Oct 2022

• A.S. Kronfeld et al. Lattice QCD and Particle Physics, 15 Jul 2022

• D. Boyda, Salvatore Calí, S. Foreman, et al., Applications of Machine Learning to Lattice Quantum Field Theory arXiv:2202.05838, Feb 2022

• S. Foreman, X.Y. Jin, J.C. Osborn, LeapFrogLayers: Trainable Framework for Effective Topological Sampling, slides, Lattice, 2021

• S. Foreman L. Jin, X.Y. Jin, A. Tomiya, J.C. Osborn, & T. Izubuchi, HMC with Normalizing Flows, slides, Lattice, 2021

• S. Foreman, X.Y. Jin, & J.C. Osborn, Deep Learning Hamiltonian Monte Carlo (+ poster) at SimDL Workshop @ ICLR, 2021

• S. Foreman, X.Y. Jin, & J.C. Osborn, Machine Learning and Neural Networks for Field Theory SnowMass, 2020

• S. Foreman Y. Meurice, J. Giedt & J. Unmuth-Yockey Examples of renormalization group transformations for image sets Physical Review E., 2018

• S. Foreman, J. Giedt, Y. Meurice, & J. Unmuth-Yockey RG inspired Machine Learning for lattice field theory arXiv:1710.02079, 2017

• S. Foreman, J. Liu, & L. Wortsmann Large Energy Density in Three-Plate Nanocapacitors due to Coulomb Blockade J. Appl. Phys, 2018

Invited Talks

• Generative Modeling and Efficient Sampling, at PASC23, July 2023

• Efficient Sampling for Lattice Gauge Theory, at Deep Fridays @ U. Bologna, April 2023

• Large Scale Training, at Introduction to AI-driven Science on Supercomputers: A Student Training Series, November 2022

• Hyperparameter Management, at 2022 ALCF Simulation, Data, and Learning Workshop, October 2022

• Statistical Learning, at ATPESC 2022, August 2022 📕 accompanying notebook

• Scientific Data Science: An Emerging Symbiosis, at Argonne National Laboratory, May 2022

• Machine Learning in HEP, at UNC Greensboro, March 2022

• Accelerated Sampling Methods for Lattice Gauge Theory, at BNL-HET & RBRC Joint Workshop “DWQ @ 25”, Dec 2021

• Training Topological Samplers for Lattice Gauge Theory, ML4HEP, on and off the Lattice @ ECT* Trento, Sep 2021

• l2hmc-qcd at the MIT Lattice Group Seminar, 2021

• Deep Learning HMC for Improved Gauge Generation to the Machine Learning Techniques in Lattice QCD Workshop, 2021

• Machine Learning for Lattice QCD at the University of Iowa, 2020

• Machine learning inspired analysis of the Ising model transition to Lattice, 2018

• Machine Learning Analysis of Ising Worms at Brookhaven National Laboratory, 2017