Oct 10 – 14, 2022
William & Mary
US/Eastern timezone
Artificial Intelligence for the Electron Ion Collider

Invited Speakers

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