Author

Sam Foreman

Published

December 3, 2024

👋 Hi, I’m Sam!

🎤 Recent Talks

[here] ( + how I make them! )

yellow pink green blue circle

import datetime
from rich import print
now = datetime.datetime.now()
day = now.strftime("%Y-%m-%d")
time = now.strftime("%H:%M:%S")
print(' '.join([
    "[#838383]Last Updated[/]:",
    f"[#E599F7]{day}[/]",
    "[#838383]@[/]",
    f"[#00CCFF]{time}[/]"
]))
Last Updated: 2024-12-01 @ 17:57:17

© Copyright Sam Foreman

You can find a full list of my publications on my Google Scholar

Boyda, Denis, Salvatore Calı̀, Sam Foreman, Lena Funcke, Daniel C Hackett, Yin Lin, Gert Aarts, et al. 2022. “Applications of Machine Learning to Lattice Quantum Field Theory.” arXiv Preprint arXiv:2202.05838.
Cheng, Scott, Jun-Liang Lin, Murali Emani, Siddhisanket Raskar, Sam Foreman, Zhen Xie, Venkatram Vishwanath, and Mahmut Taylan Kandemir. 2024. “Thorough Characterization and Analysis of Large Transformer Model Training at-Scale.” Proceedings of the ACM on Measurement and Analysis of Computing Systems 8 (1): 1–25.
Dharuman, Gautham, Logan Ward, Heng Ma, Priyanka V Setty, Ozan Gokdemir, Sam Foreman, Murali Emani, et al. 2023. “Protein Generation via Genome-Scale Language Models with Bio-Physical Scoring.” In Proceedings of the SC’23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, 95–101.
Emani, Murali, Sam Foreman, Varuni Sastry, Zhen Xie, Siddhisanket Raskar, William Arnold, Rajeev Thakur, Venkatram Vishwanath, and Michael E Papka. 2023. “A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators.” arXiv Preprint arXiv:2310.04607.
Foreman, Sam, Joel Giedt, Yannick Meurice, and Judah Unmuth-Yockey. 2018. “RG-Inspired Machine Learning for Lattice Field Theory.” In EPJ Web of Conferences, 175:11025. EDP Sciences.
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.
Foreman, Sam, Xiao-Yong Jin, and James Osborn. “MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory.” In 40th International Symposium on Lattice Field Theory (Lattice 2023) (Batavia, IL, United States, 07/31/2023 - 08/04/2023).
Foreman, Sam, Xiao-Yong Jin, and James C Osborn. 2020. “Machine Learning and Neural Networks for Field Theory.”
———. 2021a. “LeapfrogLayers: A Trainable Framework for Effective Topological Sampling.” arXiv Preprint arXiv:2112.01582.
Foreman, Sam, Xiao-Yong Jin, and James C. Osborn. 2021b. “Deep Learning Hamiltonian Monte Carlo.” https://arxiv.org/abs/2105.03418.
Foreman, Samuel Alfred. 2019. “Learning Better Physics: A Machine Learning Approach to Lattice Gauge Theory.” PhD thesis, University of Iowa.
Foreman, Samuel, Joel Giedt, Yannick Meurice, and Judah Unmuth-Yockey. 2018a. “Examples of Renormalization Group Transformations for Image Sets.” Physical Review E 98 (5): 052129.
———. 2018b. “Machine Learning Inspired Analysis of the Ising Model Transition.” In Lattice 2018.
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).
Kronfeld, Andreas S, Tanmoy Bhattacharya, Thomas Blum, Norman H Christ, Carleton DeTar, William Detmold, Robert Edwards, et al. 2022. “Lattice QCD and Particle Physics.” arXiv Preprint arXiv:2207.07641.
Liu, Jiaqi, Alfred W Hubler, Samuel Alfred Foreman, and Katharina Ott. 2017. “Energy Storage in Quantum Resonators.”
Parete-Koon, Suzanne, Michael Sandoval, Kellen Leland, Subil Abraham, Mary Ann Leung, Rebecca Hartman-Baker, Paige Kinsley, et al. 2024. “Intro to HPC Bootcamp: Engaging New Communities Through Energy Justice Projects.” Journal of Computational Science Education 15 (1).
Shanahan, Phiala, Kazuhiro Terao, and Daniel Whiteson. 2022. “Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning.” arXiv Preprint arXiv:2209.07559.
Song, Shuaiwen Leon, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, et al. 2023. “DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery Through Sophisticated AI System Technologies.” arXiv Preprint arXiv:2310.04610.
Zvyagin, Maxim, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, et al. 2023. “GenSLMs: Genome-Scale Language Models Reveal SARS-CoV-2 Evolutionary Dynamics.” The International Journal of High Performance Computing Applications 37 (6): 683–705.

See ’em all, live: Talks

Convert from HTML to slideshow version of a page by appending /slides to the end of its URL, e.g.

📆 2024

📆 2023

📆 2022

📆 2021

l2hmc-qcd at the MIT Lattice Group Seminar, 2021

📆 2020

📊 GitHub Stats

Even More !!
Wakatime

📂 saforem2/

🎪 Events

👔 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