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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>FAIR Universe</title>
<meta content="Uncertainty aware large-compute-scale AI platform for high energy physics and cosmology" name="description">
<meta content="FAIR Universe, High Energy Physics, Cosmology, Uncertainty aware" name="keywords">
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<meta property="og:title" content="FAIR Universe" />
<meta property="og:description" content="Uncertainty aware large-compute-scale AI platform for high energy physics and cosmology" />
<meta property="og:image" content="assets/img/logo.png">
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</head>
<body>
<!-- ======= Mobile nav toggle button ======= -->
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<i class="bi bi-list mobile-nav-toggle d-lg-none"></i>
<!-- ======= Header ======= -->
<header id="header" class="d-flex flex-column justify-content-center">
<nav id="navbar" class="navbar nav-menu">
<ul>
<li><a href="#hero" class="nav-link scrollto active"><i class="bi bi-house"></i> <span>Home</span></a></li>
<li><a href="#about" class="nav-link scrollto"><i class="bi bi-info-circle"></i> <span>About</span></a></li>
<li><a href="#goals" class="nav-link scrollto"><i class="bi bi-flag"></i> <span>Goals</span></a></li>
<li><a href="#news" class="nav-link scrollto"><i class="bi bi-megaphone"></i> <span>News</span></a></li>
<li><a href="#partners" class="nav-link scrollto"><i class="bi bi-people"></i> <span>Partners</span></a></li>
<li><a href="credits.html" class="nav-link scrollto"><i class="bi bi-award"></i> <span>Credits</span></a></li>
</ul>
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<!-- ======= Hero Section ======= -->
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<div class="logo-img">
<img width=300 src="assets/img/logo-white-text.svg">
</div>
<div>
<p>Uncertainty aware large-compute-scale AI platform for high energy physics and cosmology</p>
<p>
<span class="badge bg-blue text-light">Uncertainty-Aware AI</span>
<span class="badge bg-orange text-light">High Energy Physics</span>
<span class="badge bg-pink text-light">Cosmology</span>
<span class="badge bg-green text-light">Bias in Data</span>
<span class="badge bg-purple text-light">Uncertainties</span>
</p>
</div>
</div>
</section><!-- End Hero -->
<main id="main">
<!-- ======= NeurIPS Section ======= -->
<section id="about" class="about">
<div class="container" data-aos="fade-up">
<div class="section-title">
<h2>NeurIPS 2024 HiggsML Uncertainty Challenge </h2>
<a class="btn btn-lg bg-primary text-light" target="_blank" href="https://www.codabench.org/competitions/2977/"><i class="bi bi-globe2"></i> Join the Challenge here</a>
<a class="btn btn-lg bg-success text-light" target="_blank" href="https://arxiv.org/abs/2410.02867"><i class="bi bi-journal-text"></i> Read the White Paper</a>
</div>
</div>
</section>
<!-- End NeurIPS Section -->
<!-- ======= About Section ======= -->
<section id="about" class="about">
<div class="container" data-aos="fade-up">
<div class="section-title">
<h2>About</h2>
<p>
The FAIR Universe project is a collaboration between the
<a href="https://www.lbl.gov/" target="_blank"><b><code>Lawrence Berkeley National Laboratory</code></b></a>,
<a href="https://www.universite-paris-saclay.fr/en" target="_blank"><b><code>Université Paris-Saclay</code></b></a>,
<a href="https://www.washington.edu/" target="_blank"><b><code>University of Washington</code></b></a>, and
<a href="https://chalearn.org/" target="_blank"><b><code>ChaLearn</code></b></a>.
The project is funded by the
<a href="https://www.energy.gov/" target="_blank"><b><code>Department of Energy</code></b></a>
.
</p>
<p>
The collaboration is building a large-compute-scale AI platform for the hosting of large scientific datasets, models, and machine learning competitions to drive forward discoveries in high energy physics and cosmology. The initial focus is on discovering and minimizing systematic uncertainties in High Energy Physics through a sequence of challenges that will bring together researchers across physics and machine learning.
</p>
<p>
The collaboration includes scientists from Lawrence Berkeley National Laboratory, Université Paris-Saclay, the University of Washington, and ChaLearn, a non-profit that organizes challenges to stimulate research in machine learning.
</p>
</div>
</div>
</section><!-- End About Section -->
<!-- ======= Goals Section ======= -->
<section id="goals" class="about">
<div class="container" data-aos="fade-up">
<div class="section-title">
<h2>Goals</h2>
<p>
We are building an open, large-compute-scale AI ecosystem for sharing datasets, training large models, fine-tuning those models, and hosting challenges and benchmarks. Hosting of all these datasets and benchmarks will be achieved by interfacing the
<a href="https://www.nersc.gov/" target="_blank"><b><code>NERSC</code></b></a>
HPC center to
<a href="https://www.codabench.org/" target="_blank"><b><code>Codabench</code></b></a>
, a recently developed open-source platform, to provide a next generation reproducible-science AI ecosystem. This project will build the essential pieces of such an ecosystem through deployment of:
<ol>
<li>Three HEP systematic uncertainty datasets and tasks, of increasing sophistication, tailored for studies of systematic-uncertainty aware AI techniques, in particle physics and cosmology.</li>
<li>A set of HEP-AI challenges and long-lived task and algorithm benchmarks addressing compelling questions about the impact of systematic effects in AI models.</li>
<li>An HPC-enabled AI benchmark platform capable of hosting datasets and models; producing new simulated datasets; applying new AI algorithms on existing datasets; and applying uploaded AI algo- rithms on new datasets.</li>
</ol>
</p>
<p>
The collaboration with Codabench and NERSC will ensure that the project platform, benchmarks and a portfolio of algorithms will be curated and made accessible, and therefore continue to benefit the HEP community, as well as other sciences and the machine learning research community well beyond the end of the project. The research community will benefit from being exposed to well-established, empirical UQ approaches for estimation that experimenters have deployed on problems with hundreds of systematic effects. The develop- ment of principled methodologies to quantify the impact of systematic effects in the training and inference of ML models, will increase the trust of the scientific community on AI methods applied to experimental high-energy physics and beyond. The progressive structure of our challenges will bring together activity across particle physics and cosmology. Finally, both the methods and platform developed in this project will serve as a foundation for future AI challenges and benchmarks in high-energy physics, scientific and industrial applications.
</p>
</div>
</div>
</section><!-- End Goals Section -->
<!-- ======= News Section ======= -->
<section id="news" class="resume">
<div class="container" data-aos="fade-up">
<div class="section-title">
<h2>News</h2>
</div>
<div class="row">
<div class="col-lg-1"></div>
<div class="col-lg-8">
<!-- News item -->
<div class="resume-item">
<h4>Join us for the FAIR Universe - Higgs Uncertainty Challenge Workshop @ NeurIPS 2024</h4>
<span class="badge bg-gray">December 02, 2024</span>
<p><em> The workshop will be held on December 14, 2024 at the Vancouver Convention Center in Vancouver, BC, Canada as a part of the 38th annual conference on Neural Information Processing Systems (NeurIPS).
</em></p>
<ul>
<li><a href="https://fair-universe.lbl.gov/Uncertainty-Challenge-Workshop.html" target="_blank">Workshop Page</a></li>
</ul>
</div>
<!-- News item -->
<div class="resume-item">
<h4>FAIR Universe – NeurIPS 2024 Higgs Uncertainty Challenge launched on Codabench</h4>
<span class="badge bg-gray">October 01, 2024</span>
<p><em> We have launched the "FAIR Universe – NeurIPS 2024 Higgs Uncertainty Challenge" on Codabench.
</em></p>
<ul>
<li><a href="https://www.codabench.org/competitions/2977/" target="_blank">Competition Website</a></li>
</ul>
</div>
<!-- News item -->
<div class="resume-item">
<h4>FAIR Universe – the challenge of handling
uncertainties in fundamental science @ NeurIPS 2024</h4>
<span class="badge bg-gray">June 10, 2024</span>
<p><em>Our new challenge "FAIR Universe – the challenge of handling
uncertainties in fundamental science" is accepted at NeurIPS 2024 Competition Track.
</em></p>
</div>
<!-- News item -->
<div class="resume-item">
<h4>HiggsML Uncertainty Pilot Competition</h4>
<span class="badge bg-gray">March 12, 2024</span>
<p><em>We have launched the pilot version of HiggsML Uncertainty Competition today! Join the competition here:</em></p>
<ul>
<li><a href="https://www.codabench.org/competitions/2164/" target="_blank">Competition Website</a></li>
</ul>
</div>
<!-- News item -->
<div class="resume-item">
<h4>FAIR Universe @ ACAT 2024</h4>
<span class="badge bg-gray">March 07, 2024</span>
<p><em>Wahid Bhimji, FAIR Universe Project Principal Investigator, will deliver a talk about Fair Universe - HiggsML Uncertainty Challenge. Join us at the event:</em></p>
<ul>
<li><a href="https://indico.cern.ch/event/1330797/contributions/5796588/" target="_blank">Event details</a></li>
</ul>
</div>
<!-- News item -->
<div class="resume-item">
<h4>Particle Physics challenge trial</h4>
<span class="badge bg-gray">November 29, 2023</span>
<p><em>
We hosted a hackathon at the
<a href="https://indico.in2p3.fr/event/30589/" target="_blank">Artificial Intelligence and the Uncertainty Challenge in Fundamental Physics</a>
workshop on Wednesday November 29, 2023. We released a tiral version of a challenge using detailed physics simulations of accelerator data.
</em></p>
<ul>
<li><a href="https://www.codabench.org/competitions/1299/?secret_key=28d9c0fc-fe66-44c8-be89-0f2c712b4514" target="_blank">Trial Codabench Challenge</a></li>
</ul>
</div>
<!-- News item -->
<div class="resume-item">
<h4>First toy challenge</h4>
<span class="badge bg-gray">October 2, 2023</span>
<p><em>
We have designed and released our <a href="https://www.codabench.org/competitions/1152/" target="_blank">first toy challenge</a> on the Codabench platform. This challenge is designed to test the platform and provide a simple example for the community to follow. We have developed a toy dataset to illustrate the types of analyses performed on real data from particle accelerators.
</em></p>
</div>
<!-- News item -->
<div class="resume-item">
<h4>Project Announcement</h4>
<span class="badge bg-gray">November 27, 2022</span>
<p><em>The FAIR Universe project received funding as part of a $6.4 million DoE spend on AI in high energy physics:</em></p>
<ul>
<li><a href="https://cs.lbl.gov/news-media/news/2022/new-fair-universe-project-aims-to-build-supercomputer-scale-ai-benchmarks-for-hep-and-beyond/" target="_blank">LBNL announcement</a></li>
<li><a href="https://www.newswise.com/doescience/department-of-energy-announces-6-4-million-for-artificial-intelligence-research-in-high-energy-physics/?article_id=779782" target="_blank">DoE announcement</a></li>
</ul>
</div>
</div>
</div>
</div>
</section><!-- End News Section -->
<!-- ======= Partners Section ======= -->
<section id="partners" class="about">
<div class="container" data-aos="fade-up">
<div class="section-title">
<h2>Partners</h2>
</div>
<div class="row">
<div class="col-lg-1"></div>
<div class="col-lg-2 col-md-4 col-sm-6 align-items-stretch mt-4 mt-lg-0 text-center">
<a target="_blank" href="https://www.lbl.gov/"><img src="assets/img/partners/lbl.png" class="img-fluid partner-img"></a>
</div>
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<a target="_blank" href="http://www.chalearn.org/"><img src="assets/img/partners/chalearn.png" class="img-fluid partner-img"></a>
</div>
<div class="col-lg-2 col-md-4 col-sm-6 align-items-stretch mt-4 mt-lg-0 text-center">
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</div>
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</div>
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<a target="_blank" href="https://uci.edu/"><img src="assets/img/partners/UC-Irvine.png" class="img-fluid partner-img"></a>
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© Copyright <strong><span>FAIR Universe</span></strong>. All Rights Reserved
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