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About Me

I use ML and HPC to accelerate scientific discovery¹ @ 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.

 

Recent Talks

You can get a live view of some of my recent talks here

Now Playing:

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 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 UIUC.

As a member of the Data Science Group at ALCF, I work on:

• AI + Science

  • Building better sampling methods for Lattice QCD

  • GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

  • Foundation models for long term climate forecasting

• Scaling Large Language Models

• Optimizing distributed training across thousands of GPUs

• Building new parallelism techniques for efficient scaling

• Generative modeling (esp. for physical systems)

Recent Work

• DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery […], NeurIPS 2023 AI For Science Workshop, Oct 2023
  • DeepSpeed4Science.ai Blog Post
  • Loooooooong Sequence Lengths
• 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: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics, M. Zvyagin et. al., Oct 2022
  • ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research
    
• 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 Three-Plate 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 AI-driven 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 BNL-HET& RBRC Joint Workshop “DWQ @ 25”, Dec 2021
• Training Topological Samplers for Lattice Gauge Theory, ML4HEP, on and off the Lattice @ ECT* Trento, Sep 2021
• l2hmc-qcd 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

GitHub Stats (saforem2):

l2hmc-qcd

GitHub repo

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

from datetime import date
print(f"Last updated: {date.today().strftime('%d %B %y')}")

Last updated: 14 November 23

Footnotes

1. Mostly getting supercomputers to stop yelling at each other .

Citation

BibTeX citation:

@online{foreman,
  author = {Foreman, Sam},
  title = {Sam {Foreman}},
  url = {https://samforeman.me},
  langid = {en}
}

For attribution, please cite this work as:

Foreman, Sam. n.d. “Sam Foreman.” https://samforeman.me.