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title: 🪪 More
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## Sam Foreman

I am a computational scientist in the [AI / ML Group] at the
[Argonne Leadership Computing Facility (ALCF)] at
[Argonne National Laboratory].

My work centers on large-scale distributed training of foundation models
for scientific applications, with an emphasis on efficient training
systems, scaling strategies, and AI + HPC workflows.

> [!TIP] Looking for quick links?
>
> - [📬 Posts](/posts)
> - [🎙️ Talks](/talks)
> - [📚 Projects](/projects)
> - [🧑🏻‍💻 CV / Resume](/posts/resume)

## Research Focus

- Large language model training on supercomputers (Aurora, Frontier,
  LUMI, Leonardo, and others)
- Foundation models for weather and climate forecasting
- Genome-scale language models for biology
- Distributed training at extreme scale
- ML-enhanced sampling algorithms for lattice QCD

## Professional Experience

### Assistant Computational Scientist

_Argonne National Laboratory, ALCF — Lemont, IL (2022–Present)_

- Co-lead the Models and Pre-Training team for [AuroraGPT].
- Lead and contribute to large-scale AI training efforts for scientific
  workloads.
- Work with interdisciplinary teams to improve model quality, throughput,
  and scalability.

### Postdoctoral Researcher

_Argonne National Laboratory, ALCF — Lemont, IL (2019–2022)_

- Applied deep learning methods to lattice gauge theory and quantum
  field simulations.
- Developed ML-enhanced Monte Carlo methods for QCD in [`l2hmc-qcd`].
- Collaborated across national lab and university research groups.

### Graduate Researcher (DOE SCGSR Fellowship)

_Argonne National Laboratory, Mathematics and Computer Science (MCS) —
Lemont, IL (2018–2019)_

- Built and scaled [`l2hmc-qcd`] in collaboration with ALCF for doctoral
  research.

## Education

- **Ph.D., Physics** — _University of Iowa_ (2015–2019)
    - [Learning Better Physics: A Machine Learning Approach to Lattice
      Gauge Theory]
- **B.S., Engineering Physics** — _University of Illinois
  Urbana-Champaign_ (2010–2015)
    - [Energy Storage in Quantum Resonators (US Patent #US9741492B2)]
- **B.S., Applied Mathematics** — _University of Illinois
  Urbana-Champaign_ (2010–2015)

## Awards, Service, and Community

- Member, **DeepSpeed Technical Steering Committee** (2025–Present)
- Nominated to serve on the APS [Coordinating Panel for Software and
  Computing]
- **Finalist, ACM Gordon Bell Prize in Climate Modeling** (2025) for
  [AERIS]
- **Finalist, ACM Gordon Bell Prize** (2024) for [MProt-DPO]
- **ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research**
  (2022) for [GenSLM]
- **DOE Office of Science Graduate Student Research Fellow** (2018)

## Organizing

- [SC25 Workshop: High Performance Python for Science at Scale (HPPSS)]
- [SC25 Tutorial: Accelerating and Scaling Python for HPC]
- [SC24 Workshop: High Performance Python for Science at Scale (HPPSS)]
- [SC23 Workshop: High Performance Python for Science at Scale (HPPSS)]
- [Machine Learning and Quantum Computing for Earth Sciences] (USNCCM 2023)

## Contact

- Website: [samforeman.me]
- Email: [sf@omg.lol](mailto:sf@omg.lol)
- GitHub: [@saforem2](https://github.com/saforem2)
- Google Scholar: [Profile][Google Scholar]

[Argonne National Laboratory]: https://www.anl.gov
[Argonne Leadership Computing Facility (ALCF)]: https://www.alcf.anl.gov/
[AI / ML Group]: https://www.alcf.anl.gov/about/people/group/506
[AuroraGPT]: https://auroragpt.anl.gov
[`l2hmc-qcd`]: https://github.com/saforem2/l2hmc-qcd
[Learning Better Physics: A Machine Learning Approach to Lattice Gauge Theory]: https://iro.uiowa.edu/esploro/outputs/doctoral/Learning-better-physics-a-machine-learning/9983776792002771
[Energy Storage in Quantum Resonators (US Patent #US9741492B2)]: https://patents.google.com/patent/US9741492B2/en
[Coordinating Panel for Software and Computing]: https://imfisk.github.io/CPSC/
[AERIS]: https://arxiv.org/abs/2509.13523
[MProt-DPO]: https://doi.org/10.1109/SC41406.2024.00013
[GenSLM]: https://github.com/ramanathanlab/genslm
[SC25 Workshop: High Performance Python for Science at Scale (HPPSS)]: https://hppss.github.io/SC25/
[SC25 Tutorial: Accelerating and Scaling Python for HPC]: https://sc25.conference-program.com/presentation/?id=tut121&sess=sess255
[SC24 Workshop: High Performance Python for Science at Scale (HPPSS)]: https://hppss.github.io/SC24/
[SC23 Workshop: High Performance Python for Science at Scale (HPPSS)]: https://hppss.github.io/SC23/
[Machine Learning and Quantum Computing for Earth Sciences]: https://17.usnccm.org/702
[samforeman.me]: https://samforeman.me
[Google Scholar]: https://scholar.google.com/citations?user=vV_1zDwAAAAJ&hl=en
