👋 Hi, I’m Sam!

🧑🏻‍💻 About Me

• 💻 Computational scientist at Argonne National Laboratory (ALCF)

• 🧪 Interested in {AI, HPC} for science¹

  • 🚀 working on scaling large (language, vision, multi-modal) models² across thousands of GPUs

🎤 Recent Talks

live: here ( + how I make them! )

• 👀 If you’re curious

  • 🔥 What I work on

    As a member of the AI / ML Group at ALCF, I work on³:

    • 🤖 🧪 AI + Science

    • 🎲 Building better sampling methods for Lattice QCD

    • 🧬 Genome-Scale Language Models

      • GenSLM

      • 🥇 ACM Gordon Bell Special Prize

    • 🌍 Foundation models for long term climate forecasting

    • 🏃‍♂️ Scaling Large Language Models

    • 🏎️ Distributed training across thousands of GPUs

  • 📍 How I got here

    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) at 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 UIUC⁴.

📝 Work

[NOTE]: You can find a full list of my publications on my Google Scholar.

• Intro to HPC Bootcamp: Engaging New Communities Through Energy Justice Projects
  Journal of Computational Science, 2024

• Thorough Characterization and Analysis of Large Transformer Model Training At-Scale
  Proc. ACM Meas. Anal. Comput. Syst. 03/2024

• MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory
  S. Foreman et al. Lattice, 2023 (Proceedings), 12/2023

• Protein Generation via Genome-scale Language Models with Bio-physical Scoring
  @ SC’23, 11/2023

• DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery […]
  @ NeurIPS 2023 AI For Science Workshop, 10/2023

  • DeepSpeed4Science.ai Blog Post

  • Loooooooong Sequence Lengths

• Comprehensive Performance Study of LLMs on Novel AI Accelerators
  M. Emani, S. Foreman, et al., IPDPS 2024, 10/2023

• Exploratory Analysis of Climate Data with ClimRR
  S. Foreman, Intro to HPC Bootcamp @ NERSC, 08/2023

• 🏆 GenSLMs: Genome-scale language models reveal SARS-Cov-2 evolutionary dynamics
  @ SC’22 10/2022

  • 🥇 ACM Gordon Bell Special Prize

• Lattice QCD and Particle Physics
  A.S. Kronfeld et al., 07/2022

• Applications of ML to Lattice QFT
  D. Boyda, S. Calí, S. Foreman, et al., [arXiv:2202.05838], 02/2022

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

• HMC with Normalizing Flows [slides]
  S. Foreman et al., Lattice, 2021

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

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

• Examples of renormalization group transformations for image sets
  S. Foreman et al., Physical Review E., 2018

• RG inspired Machine Learning for lattice field theory
  S. Foreman et al., arXiv:1710.02079, 2017

• Large Energy Density in Three-Plate Nanocapacitors due to Coulomb Blockade
  S. Foreman et al., J. Appl. Phys, 2018

🦜 Talks

📆 2024

LLMs on Polaris @ Center for Scientific Foundation Models, Summer School 24’ [07/2024]

Parallel Training Techniques @ AI-4-Science Training Series [03/2024]

LLMs from Scratch @ LLM Tutorial Workshop [02/2024]

📆 2023

Creating Small(-ish) LLMs @ LLM Tutorial Workshop (1) [11/2023]

Exascale Science on Aurora @ Intel oneAPI Workshop @ UIC [10/2023]

LLM Lunch Talk @ ALCF Hands On HPC Workshop [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]

Efficient Sampling for LGT @ Deep Fridays @ U. Bologna [04/2023]

📆 2022

Large Scale Training @ AI4Science on Supercomputers (ALCF) [11/2022]

Hyperparameter Management @ ALCF SDL Workshop [10/2022]

Statistical Learning @ ATPESC 2022 [08/2022]

• 📕 accompanying notebook

Scientific Data Science: An Emerging Symbiosis @ ANL (05/2022)

Machine Learning in HEP @ UNC Greensboro [03/2022]

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

📆 2021

Accelerated Sampling Methods for LGT, @ DWQ @ 25 [BNL] [12/2021]

Training Topological Samplers for LGT @ ML4HEP, ECT* Trento [09/2021]

l2hmc-qcd @ MIT Lattice Group Seminar [2021]

l2hmc-qcd at the MIT Lattice Group Seminar, 2021

Deep Learning HMC for Improved Gauge Generation @ ML in LQCD Workshop [2021]

📆 2020

Machine Learning for Lattice QCD @ U. Iowa [2020]

📬 Posts

📦 Projects

📊 GitHub Stats

Even More !!

Wakatime

📂 saforem2/

🪖 Experience

🎪 Events

• Organizer for:

  • SC24 Workshop: High Performance Python for Science at Scale (HPPSS), November 2024

  • SC23 Workshop: High Performance Python for Science at Scale (HPPSS), November 2023

  • Machine Learning and Quantum Computing for Earth Sciences at 17th U. S. National Congress on Computational Mechanics, July 2023

👔 Employment

Table 1: 📟 Experience

Position	@	Start	End
Assistant Computational Scientist	ALCF	2022	–
Postdoc	ALCF	2019	2022
Graduate Researcher	ANL	2018	2019

🍎 School

Table 2: 🎓 Education

Degree	In	@	End
PhD	Physics	University of Iowa	2019
B.Sc	Physics	UIUC	2015
B.Sc	Math	UIUC	2015

🎶 Music

[updated @ 12:00:01 on 07/19/2024]

Footnotes

1. So far, for: {Lattice QCD, Quantum Mechanics, Biology (Protein Generation, Drug Discovery), and Climate Modeling / Weather Forecasting}

2. Mostly trying to get supercomputers to stop yelling at each other 🫠

3. If this sounds like something you’d be interested in doing, please feel free to reach out to me!

4. And resulted in a patent !!