Roberto Ammendola (INFN / Tor Vergata, Rome): AI for real time applications in next generation HEP detectors
Max Balandat (Meta AI, Facebook): Multi-Objective Optimization with Ax/BoTorch --- tutorial
Mihaila Bogdan (NSF): NSF perspective on opportunities for AI in nuclear physics
Mariangela Bondi (INFN / U. Catania): Streaming readout for next generation electron scattering experiment
Cameron Dean (MIT): ML for HF identification
Markus Diefenthaler (Jefferson Lab): INDRA-ASTRA
Manouchehr Farkhondeh (DOE): Perspective on opportunities for AI in Nuclear Physics
Sergey Furletov (Jefferson Lab): FastML for FPGA
Jin Huang (Brookhaven National Laboratory): Infrastructure and Frontiers in AI/ML, panelist
Simonetta Liuti (University of Virginia): ML for QCD Analysis - 3D imaging
Diana McSpadden (Jefferson Lab): ML lifecycle --- tutorial
Tony Menzo (University of Cincinnati): Modeling Hadronization Using ML and the Lund String Model
Vinicius Mikuni (National Energy Research Scientific Computing Center) Unfolding with ML (OmniFold)--- tutorial
Ben Nachman (Lawrence Berkeley National Laboratory): Differential Simulations
Connor Pecar (Duke University): ML for the Reconstruction of DIS and SIDIS Kinematics
Chao Peng (Argonne National Lab): ML particle identification with measured shower profiles from calorimetry
Yihui Ray Ren (Brookhaven National Lab): Graph Neural Networks --- tutorial
Nobuo Sato (Jefferson Lab): Femtoscale Imaging of Nuclei using ML and Exascale Platforms
Andzrej Siodmok (Jagiellonian University): Modeling Hadronization Using ML and the Cluster Model
Karthik Suresh (University of Regina): AI-assisted detector design perspectives at EIC: EPIC and Beyond.
Nhan Tran (FNAL): Machine Learning on FPGA
Fernando Torales Acosta (Lawrence Berkeley National Laboratory) Unfolding with ML (OmniFold)--- tutorial
Daniel Whiteson (University of California, Irvine): Learning to Identify Electrons
Mike Williams (MIT): Infrastructure and Frontiers in AI/ML, panelist
Tentative Talk Titles
