2nd workshop on Artificial Intelligence for the Electron Ion Collider

Contribution List

45. EIC schedule and overview

Elke-Caroline Aschenauer (BNL), Rolf Ent (Jefferson Lab)

10/10/2022, 10:25

Overview

41. DOE perspective on opportunities for AI in nuclear physics

Manouchehr Farkhondeh (US Department of Energy, Office of Nuclear Physics)

10/10/2022, 11:40

Opportunities for AI in NP and EIC

42. NSF perspective on opportunities for AI in nuclear physics

Bogdan Mihaila (National Science Foundation)

10/10/2022, 13:30

Opportunities for AI in NP and EIC

1. Tutorial on Ax/Botorch: Multi-Objective Optimization

Max Balandat (Meta/AI Facebook )

10/10/2022, 14:00

AI/ML for Design

2. Adaptive experimentation to assist detector design at EIC: from ECCE to ePIC and beyond

karthik suresh (University of Regina)

10/10/2022, 14:55

AI/ML for Design

5. AI-driven detector design for the EIC

Benjamin Nachman

10/10/2022, 15:20

AI/ML for Design

3. ML application for beam optics control in the LHC

Elena Fol (CERN)

10/10/2022, 16:00

AI/ML for Design

Particle accelerator optimization problems deal with non-linear,
multi-objective functions which depend on thousands of time-varying machine components and settings. These properties often meet the limitations of traditional optimization methods and make this problem a perfect candidate for
application of ML-based techniques. In this talk I will present, how ML can improve the control of the...

4. AI/ML overview for accelerator design activities

Todd Satogata (Jefferson Lab)

10/10/2022, 16:25

AI/ML for Design

6. ML for QCD Analysis - 3D Imaging

simonetta liuti (university of virginia)

11/10/2022, 10:00

Experiment/Theory Connections

7. Modeling Hadronization Using ML and the Lund String Model

Tony Menzo (U. Cincinnati)

11/10/2022, 10:25

Experiment/Theory Connections

8. Modeling Hadronization Using ML and the Cluster Model

Siodmok Andzrej (Jagiellonian U.)

11/10/2022, 10:50

Experiment/Theory Connections

9. A(I)DAPT

Astrid Hiller Blin (Thomas Jefferson National Accelerator Facility)

11/10/2022, 11:30

Experiment/Theory Connections

10. Femtoscale Imaging of Nuclei using ML and Exascale Platforms

nobuo sato (Jefferson Lab)

11/10/2022, 11:55

Experiment/Theory Connections

11. Differentiable Simulations

Benjamin Nachman

11/10/2022, 12:20

Experiment/Theory Connections

12. Discussion

11/10/2022, 12:45

Experiment/Theory Connections

15. Fast Detector Simulations with Machine Learning

David Shih (Rutgers)

11/10/2022, 14:00

Experiment/Theory Connections

14. Machine Learning in Spectroscopy and Partial Wave Analysis

William Phelps (Christopher Newport University/Jefferson Lab)

11/10/2022, 14:25

Experiment/Theory Connections

13. Reconstructing DIS and SIDIS properties

Connor Pecar (Duke University)

11/10/2022, 14:50

Experiment/Theory Connections

16. overview talk on ML-based unfolding

Anja Butter (Heidelberg U.)

11/10/2022, 15:35

Experiment/Theory Connections

17. Tutorial on Unfolding

Fernando Torales - Acosta, Vinicius Mikuni (Other@lbl.gov;Staff@lbl.gov;Member@lbl.gov)

11/10/2022, 16:00

Experiment/Theory Connections

18. Interpretable Networks for Identifying Leptons

Daniel Whiteson (UC Irvine)

12/10/2022, 10:00

Reconstruction and Particle Identification

23. Tagging heavy flavor jets @ RHIC

Raghav Kunnawalkam Elayavalli (Vanderbilt University)

12/10/2022, 10:30

Reconstruction and Particle Identification

20. Muon Identification with Deep Learning at EIC

William Phelps (Christopher Newport University/Jefferson Lab)

12/10/2022, 11:00

Reconstruction and Particle Identification

19. Machine Learning in ACTS

Corentin Allaire (IJCLAB)

12/10/2022, 11:28

Reconstruction and Particle Identification

21. ML particle identification with measured shower profiles from calorimetry

Chao Peng (Argonne National Laboratory)

12/10/2022, 11:48

Reconstruction and Particle Identification

22. Lambda event tagging at CLAS12

Matthew McEneaney (Duke)

12/10/2022, 12:06

Reconstruction and Particle Identification

43. ML for calorimetry

Nathan Branson (Messiah U.)

12/10/2022, 12:24

Reconstruction and Particle Identification

44. Data-driven learning: Flux+Mutability

James Giroux (U. Regina)

12/10/2022, 12:42

Reconstruction and Particle Identification

24. Tutorial on MLFlow

Kishansingh Rajput (Jefferson Lab)

12/10/2022, 14:00

Infrastructure and Frontiers

25. Foundation Model Infrastructure

Svitlana Volkova

12/10/2022, 15:00

Infrastructure and Frontiers

26. AI/ML hardware co-design

Frank Liu (ORNL)

12/10/2022, 15:18

Infrastructure and Frontiers

27. Machine Learning with FPGA

Nhan Tran (Fermilab)

12/10/2022, 15:35

Infrastructure and Frontiers

28. AI/Ml with HPC

Joo Balint (ORNL)

12/10/2022, 15:52

Infrastructure and Frontiers

29. Panel Discussion

Jin Huang (Brookhaven National Lab), Mike Williams (MIT), Tia Miceli (Fermilab)

12/10/2022, 16:15

Infrastructure and Frontiers

30. INDRA-ASTRA

Markus Diefenthaler (Jefferson Lab)

13/10/2022, 10:00

Streaming

31. Fast ML for FPGA

Sergey Furletov (JLab)

13/10/2022, 10:22

Streaming

32. AI for streaming readout: an architectural perspective

Roberto Ammendola (INFN/Roma2)

13/10/2022, 10:44

Streaming

33. AI-based data reduction for streaming DAQ

Jin Huang (Brookhaven National Lab)

13/10/2022, 11:16

Streaming

34. Streaming Readout for Next Generation Electron Scattering Experiments

Mariangela Bondi (INFN/Catania)

13/10/2022, 11:38

Streaming

35. ML for Heavy Flavor Identification

Cameron Dean (MIT)

13/10/2022, 12:00

Streaming

36. AI for Experimental Controls

Thomas Britton (JLAB)

13/10/2022, 12:22

Streaming

37. Tutorial on Graph Neural Networks

Yihui Ren (BNL)

13/10/2022, 14:00

Summary and Future

38. Hackathon on October 14

13/10/2022, 14:50

Summary and Future

39. Summaries

13/10/2022, 15:30

Summary and Future

40. Future

Cristiano Fanelli (W&M)

13/10/2022, 16:15

Summary and Future