---
layout: '@/layouts/Doc.astro'
title: '🧑🏻‍💻 Sam Foreman’s Résumé'
date: 2025-04-26
description: 'Professional resume covering education, experience, publications, and talks.'
---

Sam Foreman
2025-04-26

- [👤 About](#bust_in_silhouette-about)
- [🎓 Education](#mortar_board-education)
- [👔 Professional Experience](#necktie-professional-experience)
- [📚 Publications](#books-publications)
- [🏆 Awards and Honors](#trophy-awards-and-honors)
- [🦜 Talks](#parrot-talks)
- [🎪 Events](#circus_tent-events)
- [📓 References](#notebook-references)

## 👤 About

Computational Scientist at Argonne National Laboratory.  
Scaling AI for science on supercomputers.  
[samforeman.me](https://samforeman.me)
[GitHub](https://github.com/saforem2) • [Google
Scholar](https://scholar.google.com/citations?user=vV_1zDwAAAAJ&hl=en) •
[ORCID](https://orcid.org/0000-0002-9981-0876) •
[Twitter](https://twitter.com/saforem2)

## 🎓 Education

- **Ph.D., Physics**  
  _University of Iowa_ \| 2015–2019
    - [_Learning Better Physics: A Machine Learning Approach to Lattice
      Gauge
      Theory_](https://www.proquest.com/openview/95d7f7c12da8da8aa5ead3ac0f6ca0e8/1?cbl=18750&diss=y&pq-origsite=gscholar)
- **B.S. in Engineering Physics**  
  _University of Illinois at Urbana-Champaign_ \| 2010–2015
    - [Energy Storage in Quantum Resonators (US Patent
      \#US9741492B2)](https://patents.google.com/patent/US9741492B2/en)
- **B.S. in Applied Mathematics**  
  _University of Illinois at Urbana-Champaign_ \| 2010–2015

## 👔 Professional Experience

- **Assistant Computational Scientist**
    - _Argonne National Laboratory_, Leadership Computing Facility (ALCF)
      Lemont, IL \| 2022–Present
        - Research lead on scaling large language models (LLMs) and
          generative AI for science on supercomputers.
            - Co-lead the Models and Pretraining team of the
              [AuroraGPT](https://auroragpt.anl.gov) 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
- **Postdoctoral Researcher**
    - _Argonne National Laboratory_, 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
          ([l2hmc-qcd](https://github.com/saforem2/l2hmc-qcd)).
        - Engaged in AI-for-Science collaborations with national labs and
          university partners.
- **Graduate Researcher (DOE SCGSR Fellowship)**
    - _Argonne National Laboratory_, Mathematics and Computer Sciences
      Division (MCS)  
      Lemont, IL \| 2018 – 2019
        - Development of [l2hmc-qcd](https://github.com/saforem2/l2hmc-qcd)
          in collaboration with ALCF for my PhD Thesis research

## 📚 Publications

> [!NOTE]
>
> <span style="color:#4582ec;">
>     You can find a full list of my publications on my [Google
>     Scholar](https://scholar.google.com/citations?user=vV_1zDwAAAAJ&hl=en)
> </span>

1.  🌎 [<span class="highlight-green">**AERIS**</span>: **Argonne Earth
    Systems Model for Reliable and Skillful
    Predictions**](https://arxiv.org/abs/2509.13523) (Hatanpää et al.
    (2025))
    - ✨ [_2025 ACM Gordon Bell Prize for Climate Modeling
      Finalist_](https://awards.acm.org/bell-climate)
2.  Aurora: Architecting Argonne’s First Exascale Supercomputer for
    Accelerated Scientific Discovery (Allen et al. (2025))
3.  [HiPerRAG: High-Performance Retrieval Augmented Generation for
    Scientific Insights](https://arxiv.org/abs/2505.04846) (Gokdemir et
    al. (2025))
4.  [Automated Tuning for HMC Mass
    Ratios](https://www.osti.gov/biblio/2551828) (Torsiello et al.
    (2025))
5.  [MOFA: Discovering Materials for Carbon Capture with a GenAI and
    Simulation-Based Workflow](https://arxiv.org/abs/2501.10651) (Yan et
    al. (2025))
6.  🧪 [<span class="highlight-pink">**MProt-DPO**</span>: **Breaking
    the ExaFLOPS Barrier for Multimodal Protein Design with
    DPO**](https://doi.org/10.1109/SC41406.2024.00013) (Dharuman et al.
    (2024))
    - 🌟 [_2024 ACM Gordon Bell
      Finalist_](https://sc24.supercomputing.org/2024/10/presenting-the-finalists-for-the-2024-gordon-bell-prize/)
7.  [Intro to HPC Bootcamp: Engaging New Communities Through Energy
    Justice Projects](https://jocse.org/downloads/jocse-15-1-10.pdf)
    (<span class="nocase">Leung et al.</span> (2024))
8.  [Thorough Characterization and Analysis of Large Transformer Model
    Training At-Scale](https://doi.org/10.1145/3639034) (Cheng et al.
    (2024))
9.  [MLMC: Machine Learning Monte Carlo for Lattice Gauge
    Theory](https://arxiv.org/abs/2312.08936) (Foreman et al. (2023))
10. [Protein Generation via Genome-scale Language Models with
    Bio-physical
    Scoring](https://dl.acm.org/doi/abs/10.1145/3624062.3626087)
    (<span class="nocase">Dharuman et al.</span> (2023))
11. [DeepSpeed4Science Initiative: Enabling Large-Scale Scientific
    Discovery](https://arxiv.org/abs/2310.04610)
    (<span class="nocase">Song et al.</span> (2023))


    - [📰 DeepSpeed4Science.ai Blog
      Post](https://www.deepspeed.ai/deepspeed4science/#new-megatron-deepspeed-for-large-scale-ai4science-model-training)
    - [🚂 Loooooooong Sequence
      Lengths](/posts/auroragpt/long-sequences/)

12. [Comprehensive Performance Study of LLMs on Novel AI
    Accelerators](https://arxiv.org/abs/2310.04607) (Emani et al.
    (2023))
13. [Exploratory Analysis of Climate Data with
    `ClimRR`](https://saforem2.github.io/climate-analysis), [Intro to
    HPC Bootcamp @
    NERSC](https://github.com/NERSC/intro-HPC-bootcamp-2023) (Foreman
    (2023))
14. 🧬 [<span class="highlight">**GenSLMs**</span>: **Genome-scale
    language models reveal SARS-Cov-2 evolutionary
    dynamics**](https://www.biorxiv.org/content/10.1101/2022.10.10.511571v1.abstract)
    (<span class="nocase">Zvyagin et al.</span> (2023))
    - Winner of the [🏆 _ACM Gordon Bell Special Prize for High
      Performance Computing-Based COVID-19
      Research_](https://www.acm.org/media-center/2022/november/gordon-bell-special-prize-covid-research-2022)
15. [Lattice QCD and Particle Physics](https://arxiv.org/abs/2207.07641)
    (<span class="nocase">Kronfeld et al.</span> (2022))
16. [Applications of ML to Lattice
    QFT](https://arxiv.org/abs/2202.05838) (<span class="nocase">Boyda
    et al.</span> (2022))
17. [LeapFrogLayers: Trainable Framework for Effective
    Sampling](https://arxiv.org/abs/2112.01582) (Foreman, Izubuchi, et
    al. (2021))
18. [HMC with Normalizing Flows](https://arxiv.org/abs/2112.01586)
    \[[slides](https://indico.cern.ch/event/1006302/contributions/4380743/)\]
    (Foreman, Izubuchi, et al. (2021))
19. [Deep Learning Hamiltonian Monte
    Carlo](https://arxiv.org/abs/2105.03418) \[[+
    poster](https://simdl.github.io/posters/57-supp_DLHMC_Foreman_SimDL-ICLR2021_poster1.pdf)\]
    (Foreman, Jin, et al. (2021))
20. [Machine Learning and Neural Networks for Field
    Theory](https://bit.ly/snowmass_ml2020) (Foreman et al. (2020))
21. [Examples of renormalization group transformations for image
    sets](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.98.052129)
    (Samuel Foreman et al. (2018))
22. [RG inspired Machine Learning for lattice field
    theory](https://arxiv.org/abs/1710.02079) (Sam Foreman et al.
    (2018))
23. [Large Energy Density in Three-Plate Nanocapacitors due to Coulomb
    Blockade](https://doi.org/10.1063/1.5009698) (Hubler et al. (2018))
24. [Superconductivity of In and Sn
    Samples](https://doi.org/10.1063/1.4896340) (Deamont and Foreman
    (2014))

> [!NOTE] 📓 References
>
> <div id="refs" class="references csl-bib-body hanging-indent">
>
> <div id="ref-allen2025aurora" class="csl-entry">
>
> Allen, Benjamin S., James Anchell, Victor Anisimov, et al. 2025.
> _Aurora: Architecting Argonne’s First Exascale Supercomputer for
> Accelerated Scientific Discovery_. https://arxiv.org/abs/2509.08207.
>
> </div>
>
> <div id="ref-boyda2022applications" class="csl-entry">
>
> <span class="nocase">Boyda, Denis, Salvatore Calı̀, Sam Foreman, et al.</span>
> 2022. “Applications of Machine Learning to Lattice Quantum Field Theory.”
> *arXiv Preprint arXiv:2202.05838*. https://arxiv.org/abs/2202.05838.
>
> </div>
>
> <div id="ref-cheng2024thorough" class="csl-entry">
>
> Cheng, Scott, Jun-Liang Lin, Murali Emani, et al. 2024. “Thorough
> Characterization and Analysis of Large Transformer Model Training
> at-Scale.” _Proc. ACM Meas. Anal. Comput. Syst._ (New York, NY, USA) 8
> (1). https://doi.org/10.1145/3639034.
>
> </div>
>
> <div id="ref-deamont2014superconductivity" class="csl-entry">
>
> Deamont, George, and Sam Foreman. 2014. _Superconductivity of in and
> Sn Samples_.
>
> </div>
>
> <div id="ref-mprot-dpo2024" class="csl-entry">
>
> Dharuman, Gautham, Kyle Hippe, Alexander Brace, et al. 2024.
> “MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein
> Design Workflows with Direct Preference Optimization.” _Proceedings of
> the International Conference for High Performance Computing,
> Networking, Storage, and Analysis_ (Atlanta, GA, USA), SC ’24.
> https://doi.org/10.1109/SC41406.2024.00013.
>
> </div>
>
> <div id="ref-dharuman2023protein" class="csl-entry">
>
> <span class="nocase">Dharuman, Gautham, Logan Ward, Heng Ma, et al.</span>
> 2023. “Protein Generation via Genome-Scale Language Models with Bio-Physical
> Scoring.” *Proceedings of the SC’23 Workshops of the International Conference
> on High Performance Computing, Network, Storage, and Analysis*, 95–101.
>
> </div>
>
> <div id="ref-emani2023comprehensive" class="csl-entry">
>
> Emani, Murali, Sam Foreman, Varuni Sastry, et al. 2023. “A
> Comprehensive Performance Study of Large Language Models on Novel AI
> Accelerators.” _arXiv Preprint arXiv:2310.04607_.
> https://arxiv.org/abs/2310.04607.
>
> </div>
>
> <div id="ref-foreman2023climrr" class="csl-entry">
>
> Foreman, Sam. 2023. “Energy Justice Analysis of Climate Data with
> ClimRR.” August 7. https://saforem2.github.io/climate-analysis.
>
> </div>
>
> <div id="ref-foreman2018rg" class="csl-entry">
>
> Foreman, Sam, Joel Giedt, Yannick Meurice, and Judah Unmuth-Yockey. 2018. “<span class="nocase">RG-inspired machine learning for lattice
> field theory</span>.” _European Physical Journal Web of Conferences_,
> European physical journal web of conferences, vol. 175 (March): 11025.
> https://doi.org/10.1051/epjconf/201817511025.
>
> </div>
>
> <div id="ref-foreman2021hmc" class="csl-entry">
>
> Foreman, Sam, Taku Izubuchi, Luchang Jin, Xiao-Yong Jin, James C
> Osborn, and Akio Tomiya. 2021. “HMC with Normalizing Flows.” _arXiv
> Preprint arXiv:2112.01586_. https://arxiv.org/abs/2112.01586.
>
> </div>
>
> <div id="ref-foreman2021deep" class="csl-entry">
>
> Foreman, Sam, Xiao-Yong Jin, and Osborn James C. 2021. _Deep Learning
> Hamiltonian Monte Carlo_. https://arxiv.org/abs/2105.03418.
>
> </div>
>
> <div id="ref-foreman2020machine" class="csl-entry">
>
> Foreman, Sam, Xiao-Yong Jin, and James C Osborn. 2020. _Machine
> Learning and Neural Networks for Field Theory_.
>
> </div>
>
> <div id="ref-foreman2023mlmc" class="csl-entry">
>
> Foreman, Sam, Xiao-Yong Jin, and James C. Osborn. 2023. _MLMC: Machine
> Learning Monte Carlo for Lattice Gauge Theory_.
> https://arxiv.org/abs/2312.08936.
>
> </div>
>
> <div id="ref-foreman2018examples" class="csl-entry">
>
> Foreman, Samuel, Joel Giedt, Yannick Meurice, and Judah Unmuth-Yockey. 2018. “Examples of Renormalization Group Transformations for Image
> Sets.” _Physical Review E_ 98 (5): 052129.
>
> </div>
>
> <div id="ref-gokdemir2025hiperrag" class="csl-entry">
>
> Gokdemir, Ozan, Carlo Siebenschuh, Alexander Brace, et al. 2025.
> _HiPerRAG: High-Performance Retrieval Augmented Generation for
> Scientific Insights_. https://arxiv.org/abs/2505.04846.
>
> </div>
>
> <div id="ref-stock2025aeris" class="csl-entry">
>
> Hatanpää, Väinö, Eugene Ku, Jason Stock, et al. 2025. _AERIS: Argonne
> Earth Systems Model for Reliable and Skillful Predictions_.
> https://arxiv.org/abs/2509.13523.
>
> </div>
>
> <div id="ref-hubler2018large" class="csl-entry">
>
> Hubler, A, S Foreman, J Liu, and L Wortsmann. 2018. “Large Energy
> Density in Three-Plate Nanocapacitors Due to Coulomb Blockade.”
> _Journal of Applied Physics_ 123 (10).
>
> </div>
>
> <div id="ref-kronfeld2022lattice" class="csl-entry">
>
> <span class="nocase">
>     Kronfeld, Andreas S, Tanmoy Bhattacharya, Thomas Blum, et al.
> </span>
> 2022. “Lattice QCD and Particle Physics.” *arXiv Preprint arXiv:2207.07641*.
> https://arxiv.org/abs/2207.07641.
>
> </div>
>
> <div id="ref-leung2024intro" class="csl-entry">
>
> <span class="nocase">
>     Leung, Mary Ann, Katharine Cahill, Rebecca Hartman-Baker, et al.
> </span>
> 2024. “Intro to HPC Bootcamp: Engaging New Communities Through Energy Justice
> Projects.” *Journal of Computational Science Education* 15 (1).
> https://doi.org/10.22369/issn.2153-4136/15/1/10.
>
> </div>
>
> <div id="ref-song2023deepspeed4science" class="csl-entry">
>
> <span class="nocase">
>     Song, Shuaiwen Leon, Bonnie Kruft, Minjia Zhang, et al.
> </span>
> 2023. “DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery
> Through Sophisticated AI System Technologies.” *arXiv Preprint
> arXiv:2310.04610*. https://arxiv.org/abs/2310.04610.
>
> </div>
>
> <div id="ref-torsiello2025automated" class="csl-entry">
>
> Torsiello, J., G. T. Fleming, S. Foreman, X.-Y. Jin, and J. C. Osborn. 2025. “Automated Tuning for HMC Mass Ratios.” In _PoS_. Argonne, ALCF;
> Argonne National Laboratory (ANL), Argonne, IL (United States); Temple
> U.; Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United
> States). https://doi.org/10.22323/1.466.0052.
>
> </div>
>
> <div id="ref-yan2025mofa" class="csl-entry">
>
> Yan, Xiaoli, Nathaniel Hudson, Hyun Park, et al. 2025. _MOFA:
> Discovering Materials for Carbon Capture with a GenAI- and
> Simulation-Based Workflow_. https://arxiv.org/abs/2501.10651.
>
> </div>
>
> <div id="ref-zvyagin2023genslms" class="csl-entry">
>
> <span class="nocase">
>     Zvyagin, Maxim, Alexander Brace, Kyle Hippe, et al.
> </span>
> 2023. “GenSLMs: Genome-Scale Language Models Reveal SARS-CoV-2 Evolutionary
> Dynamics.” *The International Journal of High Performance Computing
> Applications* 37 (6): 683–705.
>
> </div>
>
> </div>

## 🏆 Awards and Honors

- Member of the DeepSpeed Technical Steering Commiittee, 2025 – Present
    - Contributing to the development and direction of the DeepSpeed
      library for large-scale model training.

- Nominated to serve on the US [**Coordinating Panel for Software and
  Computing**](https://imfisk.github.io/CPSC/) by the Division of
  Particles and Fields of the American Physical Society (APS).

- **Finalist, ACM Gordon Bell Prize in Climate Modeling**, 2025
    - Recognized for our work on  
      🌎 **AERIS** (Hatanpää et al. (2025)): The first billion-parameter
      pixel-level diffusion model for global weather and
      subseasonal-to-seasonal forecasting. Trained efficiently at scales
      from 1.3–80B parameters with our sequence-window parallelism (SWiPe)
      strategy, we achieve a sustained mixed-precision performance of
      10.21 ExaFLOPS and peak performance of 11.21 ExaFLOPS, scaling to
      10,080 nodes (120,960 GPUs) on the Aurora supercomputer.

- **Finalist, ACM Gordon Bell Prize**, 2024
    - Acknowledged for the MProt-DPO (Dharuman et al. (2024)) project,
      which achieved over 4 ExaFLOP sustained performance in multimodal
      protein design workflows using Direct Preference Optimization.
        - [Argonne team breaks new ground in AI-driven protein design –
          Argonne @
          SC](https://sc.cels.anl.gov/gordon-bell-argonne-team-breaks-new-ground-in-ai-driven-protein-design/)

- **ACM Gordon Bell Special Prize for High Performance Computing-Based
  COVID-19 Research**, 2022
    - Recognized for contributions to the GenSLMs
      (<span class="nocase">Zvyagin et al.</span> (2023)) project, which
      developed genome-scale language models to study SARS-CoV-2
      evolutionary dynamics.
        - [ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research
          Awarded to Team for Modelling How Pandemic-Causing Viruses,
          Especially SARS-CoV-2, are Identified and
          Classified](https://www.acm.org/media-center/2022/november/gordon-bell-special-prize-covid-research-2022)

- **DOE Office of Science Graduate Student Research Fellow**, 2018
    - Awarded by the Department of Energy for outstanding research
      contributions during graduate studies.

## 🦜 Talks

> [!NOTE]
>
> <span style="color:#4582ec;">
>     You can see all of my talks online at https://samforeman.me/talks/
> </span>

- 2025-:
    - 12: [Training Foundation Models on
      Supercomputers](../../talks/2025/12/16/index.html) @ Argonne
      National Laboratory
    - 10: [Training Foundation Models on
      Supercomputers](../../talks/2025/10/24/index.html) @ University of
      Illinois at Urbana-Champaign
    - 10: [Training Foundation Models on
      Supercomputers](../../talks/2025/10/15/index.html) @ Georgia
      Institute of Technology
    - 10: [AERIS: Argonne’s Earth Systems
      Model](../../talks/2025/10/08/index.html) @ [2025 ALCF Hands-On HPC
      Workshop](https://www.alcf.anl.gov/events/2025-alcf-hands-hpc-workshop)
    - 09: [Scientific AI at Scale: AI for
      Science](../../talks/openskai25/ai4science/index.html) @ [Open SkAI
      2025](https://www.openskai-conference.org)
    - 09: [Scientific AI at Scale: Distributed
      Training](../../talks/openskai25/training/index.html) @ [Open SkAI
      2025](https://www.openskai-conference.org/)
    - 07: [Large Scale Training on Diverse
      Accelerators](../../talks/AuroraGPT-SIAM25/index.html) @ [Scalable
      Deep Learning, SIAM
      AN2025](https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=84772)
    - 05: [LLMs on Aurora: 🌌
      AuroraGPT](../../talks/incite-hackathon-2025/AuroraGPT/index.html) @
      [2025 ALCF INCITE GPU
      Hackathon](https://www.alcf.anl.gov/events/alcf-incite-gpu-hackathon)
    - 05: [LLMs on Aurora: 🍋
      ezpz](../../talks/incite-hackathon-2025/ezpz/index.html) @ [2025
      ALCF INCITE GPU
      Hackathon](https://www.alcf.anl.gov/events/alcf-incite-gpu-hackathon)
    - 02: [AuroraGPT: Foundation Models for
      Science](../../talks/aurora-gpt-fm-for-electric-grid/index.html) @
      [Foundation Models for the Electric
      Grid](https://www.alcf.anl.gov/alcf-ai-science-training-series)
- 2024-:
    - 11: [Parallel Training
      Methods](../../talks/ai-for-science-2024/index.html) @
      [AI-for-Science on
      Supercomputers](https://www.alcf.anl.gov/alcf-ai-science-training-series)
    - 10:
      [AuroraGPT](../../talks/AuroraGPT/alcf-hpc-workshop-2024/index.html)
      @ [2024 ALCF Hands-On HPC
      Workshop](https://www.alcf.anl.gov/events/2024-alcf-hands-hpc-workshop)
    - 10: [Machine Learning and Foundation Models at
      Scale](../../talks/alcf-hpc-workshop-2024/index.html) @ [2024 ALCF
      Hands-On HPC
      Workshop](https://www.alcf.anl.gov/events/2024-alcf-hands-hpc-workshop)
    - 09: [AuroraGPT](../../talks/hpc-user-forum/index.html) @ [HPC User
      Forum, 2024](https://www.hpcuserforum.com/hpc-user-forum-fall-2024/)
    - 08: [Training LLMs at Scale](../../talks/llms-at-scale/) @ [ATPESC,
      2024](https://extremecomputingtraining.anl.gov/atpesc-2024/)
    - 07: [LLMs on
      Polaris](https://samforeman.me/talks/llms-on-polaris/slides) @
      [Center for Scientific Foundation Models, Summer School
      24’](https://scifm.ai/summer_school.html)
    - 03: [Parallel Training
      Techniques](https://github.com/saforem2/parallel-training-slides) @
      [AI-4-Science Training
      Series](https://github.com/argonne-lcf/ai-science-training-series/tree/main/06_parallel_training)
    - 02: [LLMs from
      Scratch](https://saforem2.github.io/llm-workshop-talk) @ [LLM
      Tutorial Workshop](https://github.com/argonne-lcf/llm-workshop)
- 2023-:
    - 11: [Creating Small(-ish)
      LLMs](https://saforem2.github.io/LLM-tutorial) @ [LLM Tutorial
      Workshop (1)](https://github.com/brettin/llm_tutorial)
    - 10: [Exascale Science on
      Aurora](https://saforem2.github.io/oneapi-talk) @ [Intel oneAPI
      Workshop @
      UIC](https://www.alcf.anl.gov/events/alcf-hands-hpc-workshop)
    - 10: [LLM Lunch Talk](https://saforem2.github.io/llm-lunch-talk) @
      [ALCF Hands On HPC
      Workshop](https://www.alcf.anl.gov/events/alcf-hands-hpc-workshop)
    - 08: [Scaling LLMs for
      Science](https://saforem2.github.io/scaling4science) @
      [Data-Intensive Computing + AI/ML at
      Scale](https://events.cels.anl.gov/event/426/overview)
    - 07: [MLMC: Machine Learning Monte
      Carlo](https://saforem2.github.io/lattice23) @ [Lattice
      2023](https://indico.fnal.gov/event/57249/contributions/271305/)
    - 07: [Generative Modeling and Efficient
      Sampling](https://saforem2.github.io/lqcd-pasc23/) @
      [PASC23](https://pasc23.pasc-conference.org/)
    - 04: [Efficient Sampling for
      LGT](https://saforem2.github.io/deep-fridays) @ [Deep Fridays @ U.
      Bologna](https://www.cs.unibo.it/~asperti/deep_fridays.html)
- 2022-:
    - 11: [Large Scale
      Training](https://saforem2.github.io/ai4sci-large-scale-training) @
      [AI4Science on Supercomputers
      (ALCF)](https://github.com/argonne-lcf/ai-science-training-series)
    - 10: [Hyperparameter
      Management](https://saforem2.github.io/hparam-management-sdl2022/) @
      [ALCF SDL
      Workshop](https://www.alcf.anl.gov/events/2022-alcf-simulation-data-and-learning-workshop)
    - 08: [Statistical
      Learning](https://saforem2.github.io/ATPESC-StatisticalLearning) @
      [ATPESC 2022](https://extremecomputingtraining.anl.gov/)
    - 05: [Scientific Data Science: An Emerging
      Symbiosis](https://saforem2.github.io/anl-job-talk/) @ ANL (05/2022)
    - 03: [Machine Learning in
      HEP](https://saforem2.github.io/physicsSeminar) @ UNC Greensboro
- 2021-:
    - 12: [Accelerated Sampling Methods for
      LGT](https://saforem2.github.io/l2hmc-dwq25/), @ [DWQ @ 25
      \[BNL\]](https://indico.bnl.gov/event/13576/)
    - 09: [Training Topological Samplers for
      LGT](https://saforem2.github.io/l2hmc_talk_ect2021) @ [ML4HEP, ECT\*
      Trento](https://indico.ectstar.eu/event/77/contributions/2349/)
    - 05: [Deep Learning HMC for Improved Gauge
      Generation](https://bit.ly/mainz21) @ [ML in LQCD
      Workshop](https://bit.ly/mainz21_overview) \[2021\]
- 2020:
    - 02: [Machine Learning for Lattice
      QCD](https://slides.com/samforeman/l2hmc-qcd/embed) @ U. Iowa
      \[2020\]

## 🎪 Events

- Organizer for:
    - [SC25 Workshop: High Performance Python for Science at Scale
      (HPPSS)](https://hppss.github.io/SC25/), November 2025
    - [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/), November 2024
    - [SC23 Workshop: High Performance Python for Science at Scale
      (HPPSS)](https://hppss.github.io/SC23/), November 2023
    - [Machine Learning and Quantum Computing for Earth
      Sciences](https://17.usnccm.org/702) at 17th U. S. National Congress
      on Computational Mechanics, July 2023

## 📓 References

> [!NOTE] 📓 References
>
> <div id="refs" class="references csl-bib-body hanging-indent">
>
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>
> Allen, Benjamin S., James Anchell, Victor Anisimov, et al. 2025.
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> Accelerated Scientific Discovery_. https://arxiv.org/abs/2509.08207.
>
> </div>
>
> <div id="ref-boyda2022applications" class="csl-entry">
>
> <span class="nocase">Boyda, Denis, Salvatore Calı̀, Sam Foreman, et al.</span>
> 2022. “Applications of Machine Learning to Lattice Quantum Field Theory.”
> *arXiv Preprint arXiv:2202.05838*. https://arxiv.org/abs/2202.05838.
>
> </div>
>
> <div id="ref-cheng2024thorough" class="csl-entry">
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> Cheng, Scott, Jun-Liang Lin, Murali Emani, et al. 2024. “Thorough
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>
> </div>
>
> <div id="ref-deamont2014superconductivity" class="csl-entry">
>
> Deamont, George, and Sam Foreman. 2014. _Superconductivity of in and
> Sn Samples_.
>
> </div>
>
> <div id="ref-mprot-dpo2024" class="csl-entry">
>
> Dharuman, Gautham, Kyle Hippe, Alexander Brace, et al. 2024.
> “MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein
> Design Workflows with Direct Preference Optimization.” _Proceedings of
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>
> </div>
>
> <div id="ref-emani2023comprehensive" class="csl-entry">
>
> Emani, Murali, Sam Foreman, Varuni Sastry, et al. 2023. “A
> Comprehensive Performance Study of Large Language Models on Novel AI
> Accelerators.” _arXiv Preprint arXiv:2310.04607_.
> https://arxiv.org/abs/2310.04607.
>
> </div>
>
> <div id="ref-foreman2023climrr" class="csl-entry">
>
> Foreman, Sam. 2023. “Energy Justice Analysis of Climate Data with
> ClimRR.” August 7. https://saforem2.github.io/climate-analysis.
>
> </div>
>
> <div id="ref-foreman2018rg" class="csl-entry">
>
> Foreman, Sam, Joel Giedt, Yannick Meurice, and Judah Unmuth-Yockey. 2018. “<span class="nocase">RG-inspired machine learning for lattice
> field theory</span>.” _European Physical Journal Web of Conferences_,
> European physical journal web of conferences, vol. 175 (March): 11025.
> https://doi.org/10.1051/epjconf/201817511025.
>
> </div>
>
> <div id="ref-foreman2021hmc" class="csl-entry">
>
> Foreman, Sam, Taku Izubuchi, Luchang Jin, Xiao-Yong Jin, James C
> Osborn, and Akio Tomiya. 2021. “HMC with Normalizing Flows.” _arXiv
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>
> </div>
>
> <div id="ref-foreman2021deep" class="csl-entry">
>
> Foreman, Sam, Xiao-Yong Jin, and Osborn James C. 2021. _Deep Learning
> Hamiltonian Monte Carlo_. https://arxiv.org/abs/2105.03418.
>
> </div>
>
> <div id="ref-foreman2020machine" class="csl-entry">
>
> Foreman, Sam, Xiao-Yong Jin, and James C Osborn. 2020. _Machine
> Learning and Neural Networks for Field Theory_.
>
> </div>
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> <div id="ref-foreman2023mlmc" class="csl-entry">
>
> Foreman, Sam, Xiao-Yong Jin, and James C. Osborn. 2023. _MLMC: Machine
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>
> </div>
>
> <div id="ref-foreman2018examples" class="csl-entry">
>
> Foreman, Samuel, Joel Giedt, Yannick Meurice, and Judah Unmuth-Yockey. 2018. “Examples of Renormalization Group Transformations for Image
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>
> </div>
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>
> </div>
>
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> _Journal of Applied Physics_ 123 (10).
>
> </div>
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