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!
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) 
Steps towards defining FAIR principles for Machine Learning (ML) BoF accepted at RDA VP18 
Towards FAIR for Machine Learning (ML) models Birds of a Feather (BoF) session accepted at SC21 Conference
First FAIR4HEP paper submitted to arXiv 
FAIR for ML Models BoF accepted at RDA VP17 
FAIR for ML Models poster at RDA VP16 
FAIR4HEP Project launches! 
FAIR4HEP is a collaboration between scientists at the University of Illinois at Urbana-Champaign, Massachusetts Institute of Technology, University of California at San Diego and the University of Minnesota
This research is supported by DE-SC0021258, DE-SC0021395, DE-SC0021225, and DE-SC0021396 from the Office of Advanced Scientific Computing Research within U.S. Department of Energy Office of Science.
