Sam Foreman’s resume, including education, experience, awards, publications, and talks.
Sam Foreman
Computational Scientist
samforeman.me
GitHub • Google Scholar • ORCID • Twitter
Résumé
🎓 Education
- Ph.D., Physics
- University of Iowa, 2019
- Learning Better Physics: A Machine Learning Approach to Lattice Gauge Theory
- University of Iowa, 2019
- B.S. in Engineering Physics
- University of Illinois at Urbana-Champaign, 2015
- Energy Storage in Quantum Resonators (US Patent #US9741492B2)
- B.S. in Applied Mathematics
- University of Illinois at Urbana-Champaign, 2015
🧑🔬 Professional Experience
- Assistant Computational Scientist
- Argonne National Laboratory, Argonne Leadership Computing Facility (ALCF)
- Lemont, IL | 2022 – Present
- Research lead on scaling large language models (LLMs) and generative AI for science on supercomputers (Aurora, Frontier, LUMI, Leonardo, …).
- Co-lead the Models and Pretraining team of the AuroraGPT project
- Optimize large-scale training of foundation models and language models for scientific applications.
- Collaborate with interdisciplinary teams to enhance simulation efficiency and scalability
- Focus on AI and HPC for scientific applications, including:
- Training large language models on supercomputers
- Genome scale language models (GenSLMs) for studying SARS-CoV-2 evolutionary dynamics
- Direct Preference Optimization (DPO) for multimodal protein design workflows
- Climate modeling and weather forecasting using foundation models
- Developing improved sampling algorithms for lattice quantum chromodynamics (QCD)
- https://www.alcf.anl.gov/about/people/sam-foreman
- Research lead on scaling large language models (LLMs) and generative AI for science on supercomputers (Aurora, Frontier, LUMI, Leonardo, …).
- Argonne National Laboratory, Argonne Leadership Computing Facility (ALCF)
- Postdoctoral Researcher
- Argonne National Laboratory, Argonne Leadership Computing Facility (ALCF)
- Lemont, IL | 2019 – 2022
- Applied deep learning to lattice gauge theory and quantum field simulations.
- Developed ML-enhanced Monte Carlo methods for QCD.
- Engaged in AI-for-Science collaborations with national labs and university partners.
- Argonne National Laboratory, Argonne Leadership Computing Facility (ALCF)
- Graduate Researcher
- Argonne National Laboratory, Math and Computer Sciences (MCS)
- Lemont, IL | 2018 – 2019
- Collaborated with ALCF while completing Ph.D., integrating ML into physical sciences workflows.
🏆 Awards and Honors
- ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research, 2022
- Recognized for contributions to the GenSLMs project, which developed genome-scale language models to study SARS-CoV-2 evolutionary dynamics.
- https://www.acm.org/media-center/2022/november/gordon-bell-special-prize-covid-research-2022
- Finalist, ACM Gordon Bell Prize, 2024
- Acknowledged for the MProt-DPO project, which achieved over 4 ExaFLOP sustained performance in multimodal protein design workflows using Direct Preference Optimization.
- https://sc.cels.anl.gov/gordon-bell-argonne-team-breaks-new-ground-in-ai-driven-protein-design/
- DOE Office of Science Graduate Student Research Fellow, 2018
- Awarded by the Department of Energy for outstanding research contributions during graduate studies.
📚 Publications1
- MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization
- GenSLMs: Genome-Scale Language Models Reveal SARS-CoV-2 Evolutionary Dynamics
- Applications of Machine Learning to Lattice Quantum Field Theory
- HMC with Normalizing Flows
- Deep Learning Hamiltonian Monte Carlo
- Examples of Renormalization Group Transformations for Image Sets
🎤 Selected Talks2
- AuroraGPT: Foundation Models for Science @ Foundation Models for the Electric Grid [02/2025]
- Parallel Training Methods @ AI-for-Science on Supercomputers [11/2024]
- AuroraGPT @ HPC User Forum, 2024 [09/2024]
- Machine Learning and Foundation Models at Scale @ 2024 ALCF Hands-On HPC Workshop [10/2024]
- Training LLMs at Scale @ ATPESC, 2024 [08/2024]
- LLMs from Scratch @ LLM Tutorial Workshop [02/2024]
- Exascale Science on Aurora @ Intel oneAPI Workshop @ UIC [10/2023]
- Scaling LLMs for Science @ Data-Intensive Computing + AI/ML at Scale [08/2023]
- MLMC: Machine Learning Monte Carlo @ Lattice 2023 [07/2023]
- Generative Modeling and Efficient Sampling @ PASC23 [07/2023]
Footnotes
See full list on Google Scholar↩︎
See full list at: samforeman.me/talks↩︎
Citation
BibTeX citation:
@online{foreman2025,
author = {Foreman, Sam},
title = {👤 {Résumé}},
date = {2025-04-26},
url = {https://samforeman.me/posts/resume/},
langid = {en}
}
For attribution, please cite this work as:
Foreman, Sam. 2025. “👤 Résumé.” April 26, 2025. https://samforeman.me/posts/resume/.