Findable, Accessible, Interoperable, and Reusable Frameworks for Physics-Inspired Artificial Intelligence in High Energy Physics

The primary focus of this project is to advance our understanding of the relationship between data and artificial intelligence (AI) models by exploring relationships among them through the development of FAIR (Findable, Accessible, Interoperable, and Reusable) frameworks. Using high-energy physics (HEP) as the science driver, this project will develop a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques, and provide an environment where novel approaches to AI can be explored.

See here for more details about the FAIR4HEP Project and meet the team!


24 Oct 2021

Working Towards Understanding the Role of FAIR for Machine Learning presented at 2nd Workshop on Data and research objects management for Linked Open Science (DaMaLOS 2021). (paper)

20 Sep 2021

Steps towards defining FAIR principles for Machine Learning (ML) BoF accepted at RDA VP18

20 Sep 2021

Towards FAIR for Machine Learning (ML) models Birds of a Feather (BoF) session accepted at SC21 Conference

06 Aug 2021

First FAIR4HEP paper submitted to arXiv

21 Apr 2021

IRIS-HEP Topical Presentation

23 Feb 2021

FAIR for ML Models BoF accepted at RDA VP17

9 Nov 2020

FAIR for ML Models poster at RDA VP16

11 August 2020

FAIR4HEP Project launches!

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