👤 Résumé

Sam Foreman’s resume, including education, experience, awards, publications, and talks.

Author

Affiliation

Sam Foreman

ALCF

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
• 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, …).
    • 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:
      • Developing improved sampling algorithms for lattice quantum chromodynamics (QCD)
      • Training large language models on supercomputers
    • https://www.alcf.anl.gov/about/people/sam-foreman
• 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.
• 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

1. See full list on Google Scholar

2. See full list at: samforeman.me/talks