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2nd workshop on Artificial Intelligence for the Electron Ion Collider

Oct 10 – 14, 2022
William & Mary, Raymond A. Mason School of Business, Alan B. Miller Hall
US/Eastern timezone
Artificial Intelligence for the Electron Ion Collider

Contacts

Contribution List

45 / 45
45. EIC schedule and overview
Elke-Caroline Aschenauer (BNL), Rolf Ent (Jefferson Lab)
10/10/22, 10:25 AM
41. DOE perspective on opportunities for AI in nuclear physics
Manouchehr Farkhondeh (US Department of Energy, Office of Nuclear Physics)
10/10/22, 11:40 AM
42. NSF perspective on opportunities for AI in nuclear physics
Bogdan Mihaila (National Science Foundation)
10/10/22, 1:30 PM
1. Tutorial on Ax/Botorch: Multi-Objective Optimization
Max Balandat (Meta/AI Facebook )
10/10/22, 2:00 PM
2. Adaptive experimentation to assist detector design at EIC: from ECCE to ePIC and beyond
karthik suresh (University of Regina)
10/10/22, 2:55 PM
5. AI-driven detector design for the EIC
Benjamin Nachman
10/10/22, 3:20 PM
3. ML application for beam optics control in the LHC
Elena Fol (CERN)
10/10/22, 4:00 PM

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)
10/10/22, 4:25 PM
6. ML for QCD Analysis - 3D Imaging
simonetta liuti (university of virginia)
10/11/22, 10:00 AM
7. Modeling Hadronization Using ML and the Lund String Model
Tony Menzo (U. Cincinnati)
10/11/22, 10:25 AM
8. Modeling Hadronization Using ML and the Cluster Model
Siodmok Andzrej (Jagiellonian U.)
10/11/22, 10:50 AM
9. A(I)DAPT
Astrid Hiller Blin (Thomas Jefferson National Accelerator Facility)
10/11/22, 11:30 AM
10. Femtoscale Imaging of Nuclei using ML and Exascale Platforms
nobuo sato (Jefferson Lab)
10/11/22, 11:55 AM
11. Differentiable Simulations
Benjamin Nachman
10/11/22, 12:20 PM
12. Discussion
10/11/22, 12:45 PM
15. Fast Detector Simulations with Machine Learning
David Shih (Rutgers)
10/11/22, 2:00 PM
14. Machine Learning in Spectroscopy and Partial Wave Analysis
William Phelps (Christopher Newport University/Jefferson Lab)
10/11/22, 2:25 PM
13. Reconstructing DIS and SIDIS properties
Connor Pecar (Duke University)
10/11/22, 2:50 PM
16. overview talk on ML-based unfolding
Anja Butter (Heidelberg U.)
10/11/22, 3:35 PM
17. Tutorial on Unfolding
Fernando Torales - Acosta, Vinicius Mikuni (Other@lbl.gov;Staff@lbl.gov;Member@lbl.gov)
10/11/22, 4:00 PM
18. Interpretable Networks for Identifying Leptons
Daniel Whiteson (UC Irvine)
10/12/22, 10:00 AM
23. Tagging heavy flavor jets @ RHIC
Raghav Kunnawalkam Elayavalli (Vanderbilt University)
10/12/22, 10:30 AM
20. Muon Identification with Deep Learning at EIC
William Phelps (Christopher Newport University/Jefferson Lab)
10/12/22, 11:00 AM
19. Machine Learning in ACTS
Corentin Allaire (IJCLAB)
10/12/22, 11:28 AM
21. ML particle identification with measured shower profiles from calorimetry
Chao Peng (Argonne National Laboratory)
10/12/22, 11:48 AM
22. Lambda event tagging at CLAS12
Matthew McEneaney (Duke)
10/12/22, 12:06 PM
43. ML for calorimetry
Nathan Branson (Messiah U.)
10/12/22, 12:24 PM
44. Data-driven learning: Flux+Mutability
James Giroux (U. Regina)
10/12/22, 12:42 PM
24. Tutorial on MLFlow
Kishansingh Rajput (Jefferson Lab)
10/12/22, 2:00 PM
25. Foundation Model Infrastructure
Svitlana Volkova
10/12/22, 3:00 PM
26. AI/ML hardware co-design
Frank Liu (ORNL)
10/12/22, 3:18 PM
27. Machine Learning with FPGA
Nhan Tran (Fermilab)
10/12/22, 3:35 PM
28. AI/Ml with HPC
Joo Balint (ORNL)
10/12/22, 3:52 PM
29. Panel Discussion
Jin Huang (Brookhaven National Lab), Mike Williams (MIT), Tia Miceli (Fermilab)
10/12/22, 4:15 PM
30. INDRA-ASTRA
Markus Diefenthaler (Jefferson Lab)
10/13/22, 10:00 AM
31. Fast ML for FPGA
Sergey Furletov (JLab)
10/13/22, 10:22 AM
32. AI for streaming readout: an architectural perspective
Roberto Ammendola (INFN/Roma2)
10/13/22, 10:44 AM
33. AI-based data reduction for streaming DAQ
Jin Huang (Brookhaven National Lab)
10/13/22, 11:16 AM
34. Streaming Readout for Next Generation Electron Scattering Experiments
Mariangela Bondi (INFN/Catania)
10/13/22, 11:38 AM
35. ML for Heavy Flavor Identification
Cameron Dean (MIT)
10/13/22, 12:00 PM
36. AI for Experimental Controls
Thomas Britton (JLAB)
10/13/22, 12:22 PM
37. Tutorial on Graph Neural Networks
Yihui Ren (BNL)
10/13/22, 2:00 PM
38. Hackathon on October 14
10/13/22, 2:50 PM
39. Summaries
10/13/22, 3:30 PM
40. Future
Cristiano Fanelli (W&M)
10/13/22, 4:15 PM
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