Last Updated: 02/12/2024 @ 19:06:38
About Me
I use ML and HPC to accelerate scientific discovery^{1} @ ALCF.
I’m generally interested in the application of machine learning to computational problems in science, particularly within the context of high performance computing.
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) from 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.
As a member of the Data Science Group at ALCF, I work on:
Building new parallelism techniques for efficient scaling
Generative modeling (esp. for physical systems)
Projects

ezpz

Distributed training,
ezpz
. 
MegatronDeepSpeed
 MegatronLM + DeepSpeed, for the largest of large language models.

wordplay
[web] 
Built on
[ nanoGPT
] with support for^{2}
{
:hugging_face:datasets
,DeepSpeed
}

aisciencetrainingseries
[web]  Student training series on AIdriven Science on Supercomputers

enrich

Python’s
logging
, with Rich 
ambivalent
[web]  Minimal, beautiful (+ highlycustomizable) styles for Matplotlib^{3}.

l2hmcqcd
[web]  Application of the L2HMC algorithm to simulations in lattice QCD.

climateanalysis
[web]  Climate Analysis project using ClimRR data
Recent Work
 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, 2023
 GenSLMs: Genomescale language models reveal SARSCoV2 evolutionary dynamics, M. Zvyagin et. al., 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
Recent Talks
 MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory, at Lattice 2023, July 2023
 Generative Modeling and Efficient Sampling, at PASC23, July 2023
 Efficient Sampling for Lattice Gauge Theory, at Deep Fridays @ U. Bologna, April 2023
 Large Scale Training, at Introduction to AIdriven Science on Supercomputers: A Student Training Series, November 2022
 Hyperparameter Management, at 2022 ALCF Simulation, Data, and Learning Workshop, October 2022
 Statistical Learning, at ATPESC 2022, August 2022 📕 accompanying notebook
 Scientific Data Science: An Emerging Symbiosis, at Argonne National Laboratory, May 2022
 Machine Learning in HEP, at UNC Greensboro, March 2022
 Accelerated Sampling Methods for Lattice Gauge Theory, at BNLHET& RBRC Joint Workshop “DWQ @ 25”, Dec 2021
 Training Topological Samplers for Lattice Gauge Theory, ML4HEP, on and off the Lattice @ ECT* Trento, Sep 2021
 l2hmcqcd at the MIT Lattice Group Seminar, 2021
 Deep Learning HMC for Improved Gauge Generation to the Machine Learning Techniques in Lattice QCD Workshop, 2021
 Machine Learning for Lattice QCD at the University of Iowa, 2020
 Machine learning inspired analysis of the Ising model transition to Lattice, 2018
 Machine Learning Analysis of Ising Worms at Brookhaven National Laboratory, 2017
Active Projects
Experience
Assistant Computational Scientist  ALCF  2022  –  
Postdoc  ALCF  2019  2022  
Graduate Researcher  ANL  2018  2019 
Education
PhD  Physics  University of Iowa  2019 
B.Sc  Physics  UIUC  2015 
B.Sc  Math  UIUC  2015 
Events
Organizer for Machine Learning and Quantum Computing for Earth Sciences at 17th U. S. National Congress on Computational Mechanics, July 2023
Organizer for SC23 Workshop: High Performance Python for Science at Scale (HPPSS), November 2023
Appendix
Footnotes
Mostly getting supercomputers to stop yelling at each other .↩︎
Forked from
saforem2/opinionated
Citation
@online{foreman,
author = {Foreman, Sam},
title = {Sam {Foreman}},
url = {https://samforeman.me},
langid = {en}
}