GitHub - saforem2/personal_site: My personal website

Sam Foreman 2024-12-25

👋 Hi, I’m Sam!

🧑🏻‍💻 About

Computational scientist @ Argonne National Laboratory (ALCF)
Working on:
- 🧪 {AI, HPC} for science
- 🚀 training large models on supercomputers

[!TIP]

🎤 Recent Talks

[here] ( + how I make them! )

[!TIP]

<img src="./assets/spotify-green.svg" class="inline-icon"

style="height:1.25rem;vertical-align:text-bottom;" /> Now Playing

[!TIP]

<script> /** Developed by Prashant Shrestha + https://prashant.me */ var lastfmData = { baseURL: "https://ws.audioscrobbler.com/2.0/?method=user.getrecenttracks&user=", // Your Last.fm Username user: "saforem2", // Your API key api_key: "1dbc15037c1fe71ce06acbb3f73adc75", additional: "&format=json&limit=1" }; var getSetLastFM = function() { $.ajax({ type: "GET", url: lastfmData.baseURL + lastfmData.user + "&api_key=" + lastfmData.api_key + lastfmData.additional, dataType: "json", success: function(resp) { var recentTrack = resp.recenttracks.track[0]; var formatted = // "" + recentTrack.name; "🎶 " + recentTrack.name; $("a#tracktitle") .html(formatted) .attr("href", recentTrack.url) .attr("title", recentTrack.name + " by " + recentTrack.artist["#text"]) .attr("target", "_blank"); var artistFormatted = // "" + recentTrack.artist["#text"]; "🗣️ " + recentTrack.artist["#text"]; $("a#trackartist") .html(artistFormatted) .attr("title", "Artist : " + recentTrack.artist["#text"]); $("img#trackart").attr("src", recentTrack.image[2]["#text"]); }, error: function(resp) { $("a#tracktitle").html( "" + "Silence!" ); $("img#trackart").attr("src", "🧑🏻‍💻"); var artistFormatted = "Sam Foreman"; $("a#trackartist") .html(artistFormatted) .attr("href", "https://samforeman.me"); } }); }; // Get the new one. getSetLastFM(); // Start the countdown. setInterval(getSetLastFM, 10 * 5000); </script>

[!TIP]

❤️‍🩹 Status

👀 If you’re curious

🔥 What I work on

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

🤖 🧪 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 UIUC.

This work ultimately resulted in a patent !!

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: 2024-12-25 @ 01:59:51
© Copyright Sam Foreman

📝 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

[!TIP]

📓 References

References:

(Sam Foreman et al. 2018)

(Hubler et al. 2018)

(Samuel Foreman et al. 2018a)

(Samuel Foreman et al. 2018b)

(S. A. Foreman 2019)

(Sam Foreman, Jin, and Osborn 2020)

(Sam Foreman, Jin, and Osborn 2021b)

(Sam Foreman et al. 2021)

(Sam Foreman, Jin, and Osborn 2021a)

(Liu et al. 2017)

(Boyda et al. 2022)

(Kronfeld et al. 2022)

(Zvyagin et al. 2023)

(Sam Foreman, Jin, and Osborn)

(Deamont and Foreman 2014)

(Emani et al. 2023)

(Song et al. 2023)

(Dharuman et al. 2023)

(Shanahan, Terao, and Whiteson 2022)

(Cheng et al. 2024)

(Parete-Koon et al. 2024)

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.

Deamont, George, and Sam Foreman. 2014. “Superconductivity of in and Sn Samples.”

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.

🦜 Talks

[!NOTE]

See ’em all, live: Talks

[!TIP]

HTML ⇆ Reveal.js

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

HTML: https://samforeman.me/talks/ai-for-science-2024/

Slides: https://samforeman.me/talks/ai-for-science-2024/slides

📆 2024

[!TIP]

Parallel Training Methods @ AI-for-Science on Supercomputers [11/2024]

<iframe class="slide-deck reveal-full-page" loading="lazy" allow="picture-in-picture" src="/talks/ai-for-science-2024/slides.html" title="Parallel Training Methods" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

AuroraGPT @ 2024 ALCF Hands-On HPC Workshop [10/2024]

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="/talks/AuroraGPT/alcf-hpc-workshop-2024/slides.html" title="Machine Learning and Foundation Models at Scale" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

Machine Learning and Foundation Models at Scale @ 2024 ALCF Hands-On HPC Workshop [10/2024]

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://samforeman.me/talks/alcf-hpc-workshop-2024/slides#/section" title="Machine Learning and Foundation Models at Scale" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

AuroraGPT @ HPC User Forum, 2024 [09/2024]
<iframe class="slide-deck reveal-full-page" loading="lazy" allow="picture-in-picture" src="/talks/hpc-user-forum/slides.html" title="AuroraGPT" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

Training LLMs at Scale @ ATPESC, 2024 [08/2024]
<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="/talks/llms-at-scale/slides.html" title="Training LLMs at Scale" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

LLMs on Polaris @ Center for Scientific Foundation Models, Summer School 24’ [07/2024]
<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="/talks/llms-on-polaris/slides.html" title="LLMs on Polaris" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/parallel-training-slides" title="Parallel Training Techniques" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/llm-workshop-talk" title="LLMs from Scratch" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

📆 2023

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/LLM-tutorial" title="Creating Small(-ish) LLMs" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen> </iframe>

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/oneapi-talk" title="Exascale Science on Aurora" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen> </iframe>

[!TIP]

LLM Lunch Talk @ ALCF Hands On HPC Workshop [10/2023]

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/llm-lunch-talk/#/section" title="LLMs on Polaris" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

Scaling LLMs for Science @ Data-Intensive Computing + AI/ML at Scale [08/2023]

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/scaling4science/#/section" title="Scaling LLMs for Science and Ongoing Collaborations" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

MLMC: Machine Learning Monte Carlo @ Lattice 2023 [07/2023]

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/lattice23/#/title-slide" title="MLMC: Machine Learning Monte Carlo" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

Generative Modeling and Efficient Sampling @ PASC23 [07/2023]

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/lqcd-pasc23/" title="Generative Modeling and Efficient Sampling" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/deep-fridays/" title="Efficient Sampling for LGT" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

📆 2022

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/ai4sci-large-scale-training/#" title="Large Scale Training" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

Hyperparameter Management @ ALCF SDL Workshop [10/2022]

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/hparam-management-sdl2022" title="Hyperparameter Management" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

Statistical Learning @ ATPESC 2022 [08/2022]

📕 accompanying notebook

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/ATPESC-StatisticalLearning/#/" title="Statistical Learning" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/anl-job-talk" title="Scientific Data Science" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

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

Machine Learning in HEP, at UNC Greensboro, March 2022

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/physicsSeminar" title="Machine Learning in HEP" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="width:100%!important; border:none;border-radius:0.25rem;" style="aspect-ratio:1.3671875;"> </iframe>

📆 2021

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/l2hmc-dwq25/" title="Accelerated Sampling Methods for LGT" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://saforem2.github.io/l2hmc_talk_ect2021" title="Training Topological Samplers for LGT" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

[!TIP]

l2hmc-qcd @ MIT Lattice Group Seminar [2021]

l2hmc-qcd at the MIT Lattice Group Seminar, 2021

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://slides.com/samforeman/dlhmc/embed" title="Deep Learning HMC for Improved Gauge Generation" scrolling="no" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

📆 2020

[!TIP]

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

<iframe class="slide-deck" loading="lazy" allow="picture-in-picture" src="https://slides.com/samforeman/l2hmc-qcd/embed" title="Machine Learning for Lattice QCD" align="center" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="aspect-ratio:1.3671875;"> </iframe>

📬 Posts

📦 Projects

[!TIP]

📊 GitHub Stats

Even More !!

Wakatime

[!TIP]

📂 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

📟 Experience

🍎 School

Table 2: 🎓 Education

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

🎓 Education

🎶 Music

[!TIP]

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🧑🏻‍💻 About

🎤 Recent Talks

<img src="./assets/spotify-green.svg" class="inline-icon"

❤️‍🩹 Status

📝 Work

📓 References

🦜 Talks

HTML ⇆ Reveal.js

📆 2024

Parallel Training Methods @ AI-for-Science on Supercomputers [11/2024]

AuroraGPT @ 2024 ALCF Hands-On HPC Workshop [10/2024]

Machine Learning and Foundation Models at Scale @ 2024 ALCF Hands-On HPC Workshop [10/2024]

AuroraGPT @ HPC User Forum, 2024 [09/2024]

Training LLMs at Scale @ ATPESC, 2024 [08/2024]

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]

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

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

📆 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]

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

📂 saforem2/

🪖 Experience

🎪 Events

👔 Employment

🍎 School

🎶 Music

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