Last Updated: 04/18/2024 @ 20:59:55
👋 Sam, I am
I am:
currently a computational scientist at Argonne National Laboratory (ALCF).
generally interested in the application of AI + HPC to computational problems in science^{1},
usually working on scaling large (language, vision, multimodal) models across thousands of GPUs 🚀
 If this sounds like something you’d be interested in doing, please feel free to reach out to me
📍 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 UrbanaChampaign. 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 UIUC^{2}.
🤔 What I work on
As a member of the AI / ML Group at ALCF, I work on:
Building new parallelism techniques for efficient scaling
Generative modeling (esp. for physical systems)
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 AtScale, Proc. ACM Meas. Anal. Comput. Syst. 8 March 2024
MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory, S. Foreman, X.Y. Jin, & J.C. Osborn, Lattice, 2023 (Proceedings), Dec 2023
Protein Generation via Genomescale Language Models with Biophysical Scoring, SC’23, Nov 2023
DeepSpeed4Science Initiative: Enabling LargeScale Scientific Discovery […], NeurIPS 2023 AI For Science Workshop, Oct 2023 A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators, M. Emani, S. Foreman, et al., IPDPS 2024, Oct 2023
Exploratory Analysis of Climate Data with
ClimRR
, S. Foreman, Intro to HPC Bootcamp @ NERSC, August 7, 2023GenSLMs: Genomescale language models reveal SARSCoV2 evolutionary dynamics, Oct 2022
Lattice QCD and Particle Physics, A.S. Kronfeld et al., July 15, 2022
Applications of ML to Lattice QFT, arXiv:2202.05838, D. Boyda, S. Calí, S. Foreman, et al., Feb 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 ThreePlate Nanocapacitors due to Coulomb Blockade, S. Foreman et al., J. Appl. Phys, 2018
🪧 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
)🪧 Generative Modeling and Efficient Sampling, @ PASC23, (
07/2023
)🪧 Large Scale Training, @ AI4Science on Supercomputers, (
02/2023
)🪧 Statistical Learning, @ ATPESC 2022, (
08/2022
) 📕 accompanying notebook🪧 Scientific Data Science: An Emerging Symbiosis @ Argonne National Laboratory, (
05/2022
)🪧 Machine Learning in HEP, @ UNC Greensboro, (
03/2022
)🪧 Training Topological Samplers for Lattice Gauge Theory @ ML4HEP @ ECT* Trento, (
09/2021
)🪧 l2hmcqcd @ MIT Lattice Group Seminar, (
2021
)🪧 Machine Learning for Lattice QCD @ University of Iowa, (
2020
) Machine Learning Analysis of Ising Worms @ Brookhaven National Laboratory, (
2017
)

ezpz
✨ [web] 
Distributed training,
ezpz

MegatronDeepSpeed
 For the largest of large language models.

wordplay
🎮💬 [web] 
[
nanoGPT
]^{3}+{🤗
datasets
,DeepSpeed
}

aisciencetrainingseries
📝 [web]  Lecture series, “AI4ScienceonSupercomputers”

enrich
💸 [web] 
Python’s
logging
, with Rich 
ambivalent
🤷🏻♂️ [web]  Clean, simple style for Matplotlib figures^{4}.

l2hmcqcd
🔲 [web]  Accelerated samplers for Lattice QCD

climateanalysis
🌍 [web]  Climate Analysis project using ClimRR data
GitHub Stats
Organizer for SC23 Workshop: High Performance Python for Science at Scale (HPPSS), November 2023
Organizer for Machine Learning and Quantum Computing for Earth Sciences at 17th U. S. National Congress on Computational Mechanics, July 2023
Assistant Computational Scientist  ALCF  2022  –  
Postdoc  ALCF  2019  2022  
Graduate Researcher  ANL  2018  2019 
Footnotes
Citation
@online{foreman2024,
author = {Foreman, Sam},
title = {Sam {Foreman}  {Personal} {Website}},
date = {20240418},
url = {https://samforeman.me},
langid = {en}
}