Welcome & Introduction

• Kevin Brown (C-AD)

Overview of polarized proton acceleration and highlights of the current RHIC run

• Vincent Schoefer (CAD)

Status of accurate accelerator models for L2B, Booster, BtL, and AGS

• Eiad Hamwi (Cornell University)

Experimental examples of Bayesian Optimization at BNL

• Lucy Lin (Cornell University)

Status of bunch-merge simulations and experiments using Reinforcement Learning

• Yuan Gao

Overview of Uncertainty Quantification and application to p-p acceleration

• Nathan Urban (Brookhaven National Laboratory)

Basis of Bayesian Optimization - connecting theory to experiment

• Yinan Wang

Application options of the JLAB approach to reinforcement learning

• Armen Kasparian

Connecting Bmad and BNL control systems - Digital Twin preparations

• Kiel Hock (Brookhaven National Laboratory)

Applications of international ML experiences in the Linac to RHIC chain

• Auralee Edelen (SLAC/Stanford)

Bmad Julia - what new code do ML applications need

• David Sagan (faculty@cornell.edu;member@cornell.edu;alum@cornell.edu;employee@cornell.edu)

Improved emittance measurements

• Jon Edelen (RadiaSoft)

Steps toward digital twins

• Lucy Lin (Cornell University)

Summary, and brain storming

• Georg Hoffstaetter (Cornell)