Machine Learning Collaboration Meeting

Contribution List

1. Welcome & Introduction

Kevin Brown (C-AD)

8/21/24, 8:30 AM

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

Vincent Schoefer (CAD)

8/21/24, 9:00 AM

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

Eiad Hamwi (Cornell University)

8/21/24, 9:45 AM

4. Experimental examples of Bayesian Optimization at BNL

Lucy Lin (Cornell University)

8/21/24, 11:00 AM

14. Status of bunch-merge simulations and experiments using Reinforcement Learning

Yuan Gao

8/21/24, 11:45 AM

5. Overview of Uncertainty Quantification and application to p-p acceleration

Nathan Urban (Brookhaven National Laboratory)

8/21/24, 4:00 PM

6. Basis of Bayesian Optimization - connecting theory to experiment

Yinan Wang

8/21/24, 4:30 PM

8. Application options of the JLAB approach to reinforcement learning

Armen Kasparian

8/22/24, 8:30 AM

7. Connecting Bmad and BNL control systems - Digital Twin preparations

Kiel Hock (Brookhaven National Laboratory)

8/22/24, 9:15 AM

9. Applications of international ML experiences in the Linac to RHIC chain

Auralee Edelen (SLAC/Stanford)

8/22/24, 10:00 AM

10. Bmad Julia - what new code do ML applications need

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

8/22/24, 11:00 AM

11. Improved emittance measurements

Jon Edelen (RadiaSoft)

8/22/24, 11:45 AM

12. Steps toward digital twins

Lucy Lin (Cornell University)

8/22/24, 1:30 PM

13. Summary, and brain storming

Georg Hoffstaetter (Cornell)

8/22/24, 2:00 PM