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 is developing a FAIR framework to advance our understanding of AI, providing new insights to apply AI techniques, and providing an environment where novel approaches to AI can be explored.
This project is an interdisciplinary, multi-department, and multi-institutional effort. This project aligns with the DOE’s initiative to advance FAIR data principles that focuses on making AI data and models more accessible and reusable by application developers and researchers to further accelerate AI research and development. Through this award, the interdisciplinary and multi-institutional team of experts are working to lead the definition and adoption of FAIR principles for AI models and data in the context of HEP.
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.
