Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

no code implementations • 8 Jan 2025 • J. Kvapil, G. Borca-Tasciuc, H. Bossi, K. Chen, Y. Chen, Y. Corrales Morales, H. Da Costa, C. Da Silva, C. Dean, J. Durham, S. Fu, C. Hao, P. Harris, O. Hen, H. Jheng, Y. Lee, P. Li, X. Li, Y. Lin, M. X. Liu, V. Loncar, J. P. Mitrevski, A. Olvera, M. L. Purschke, J. S. Renck, G. Roland, J. Schambach, Z. Shi, N. Tran, N. Wuerfel, B. Xu, D. Yu, H. Zhang

This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC).

Graph Neural Network High-Level Synthesis

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan"

no code implementations • 5 Nov 2019 • J. Amundson, J. Annis, C. Avestruz, D. Bowring, J. Caldeira, G. Cerati, C. Chang, S. Dodelson, D. Elvira, A. Farahi, K. Genser, L. Gray, O. Gutsche, P. Harris, J. Kinney, J. B. Kowalkowski, R. Kutschke, S. Mrenna, B. Nord, A. Para, K. Pedro, G. N. Perdue, A. Scheinker, P. Spentzouris, J. St. John, N. Tran, S. Trivedi, L. Trouille, W. L. K. Wu, C. R. Bom

Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership.