Name: 2nd workshop on Artificial Intelligence for the Electron Ion Collider
Start: 2022-10-10T10:00:00-04:00
End: 2022-10-14T18:00:00-04:00
Location: William & Mary, Raymond A. Mason School of Business, Alan B. Miller Hall

2nd workshop on Artificial Intelligence for the Electron Ion Collider

from Monday, 10 October 2022 (10:00) to Friday, 14 October 2022 (18:00)

Monday, 10 October 2022

10:00

10:00 - 10:25
Room: room 1019
10:25
10:25 - 11:20
Room: room 1019
Contributions

10:25 EIC schedule and overview - Elke-Caroline Aschenauer (BNL) Rolf Ent (Jefferson Lab)
11:20 Coffee break
Coffee break
11:20 - 11:40
Room: room 1019
11:40
11:40 - 12:30
Room: room 1019
Contributions

11:40 DOE perspective on opportunities for AI in nuclear physics - Manouchehr Farkhondeh (US Department of Energy, Office of Nuclear Physics)
12:30 Lunch (on your own)
Lunch (on your own)
12:30 - 13:30
Room: room 1019
13:30
13:30 - 14:00
Room: room 1019
Contributions

13:30 NSF perspective on opportunities for AI in nuclear physics - Bogdan Mihaila (National Science Foundation)
14:00
14:00 - 17:00
Room: room 1019
Contributions

14:00 Tutorial on Ax/Botorch: Multi-Objective Optimization - Max Balandat (Meta/AI Facebook )

14:55 Adaptive experimentation to assist detector design at EIC: from ECCE to ePIC and beyond - karthik suresh (University of Regina)

15:20 AI-driven detector design for the EIC - Benjamin Nachman

16:00 ML application for beam optics control in the LHC - Elena Fol (CERN)

16:25 AI/ML overview for accelerator design activities - Todd Satogata (Jefferson Lab)
Tuesday, 11 October 2022
10:00
10:00 - 13:00
Room: room 1019
Contributions

10:00 ML for QCD Analysis - 3D Imaging - simonetta liuti (university of virginia)

10:25 Modeling Hadronization Using ML and the Lund String Model - Tony Menzo (U. Cincinnati)

10:50 Modeling Hadronization Using ML and the Cluster Model - Siodmok Andzrej (Jagiellonian U.)

11:30 A(I)DAPT - Astrid Hiller Blin (Thomas Jefferson National Accelerator Facility)

11:55 Femtoscale Imaging of Nuclei using ML and Exascale Platforms - nobuo sato (Jefferson Lab)

12:20 Differentiable Simulations - Benjamin Nachman

12:45 Discussion
13:00 Lunch (on your own)
Lunch (on your own)
13:00 - 14:00
Room: room 1019
14:00
14:00 - 17:00
Room: room 1019
Contributions

14:00 Fast Detector Simulations with Machine Learning - David Shih (Rutgers)

14:25 Machine Learning in Spectroscopy and Partial Wave Analysis - William Phelps (Christopher Newport University/Jefferson Lab)

14:50 Reconstructing DIS and SIDIS properties - Connor Pecar (Duke University)

15:35 overview talk on ML-based unfolding - Anja Butter (Heidelberg U.)

16:00 Tutorial on Unfolding - Fernando Torales - Acosta Vinicius Mikuni (Other@lbl.gov;Staff@lbl.gov;Member@lbl.gov)
Wednesday, 12 October 2022
10:00
10:00 - 13:00
Room: room 1019
Contributions

10:00 Interpretable Networks for Identifying Leptons - Daniel Whiteson (UC Irvine)

10:30 Tagging heavy flavor jets @ RHIC - Raghav Kunnawalkam Elayavalli (Vanderbilt University)

11:00 Muon Identification with Deep Learning at EIC - William Phelps (Christopher Newport University/Jefferson Lab)

11:28 Machine Learning in ACTS - Corentin Allaire (IJCLAB)

11:48 ML particle identification with measured shower profiles from calorimetry - Chao Peng (Argonne National Laboratory)

12:06 Lambda event tagging at CLAS12 - Matthew McEneaney (Duke)

12:24 ML for calorimetry - Nathan Branson (Messiah U.)

12:42 Data-driven learning: Flux+Mutability - James Giroux (U. Regina)
13:00 Lunch (on your own)
Lunch (on your own)
13:00 - 14:00
Room: room 1019
14:00
14:00 - 17:00
Room: room 1019
Contributions

14:00 Tutorial on MLFlow - Kishansingh Rajput (Jefferson Lab)

15:00 Foundation Model Infrastructure - Svitlana Volkova

15:18 AI/ML hardware co-design - Frank Liu (ORNL)

15:35 Machine Learning with FPGA - Nhan Tran (Fermilab)

15:52 AI/Ml with HPC - Joo Balint (ORNL)

16:15 Panel Discussion - Jin Huang (Brookhaven National Lab) Mike Williams (MIT) Tia Miceli (Fermilab)
Thursday, 13 October 2022
10:00
10:00 - 13:00
Room: room 1019
Contributions

10:00 INDRA-ASTRA - Markus Diefenthaler (Jefferson Lab)

10:22 Fast ML for FPGA - Sergey Furletov (JLab)

10:44 AI for streaming readout: an architectural perspective - Roberto Ammendola (INFN/Roma2)

11:16 AI-based data reduction for streaming DAQ - Jin Huang (Brookhaven National Lab)

11:38 Streaming Readout for Next Generation Electron Scattering Experiments - Mariangela Bondi (INFN/Catania)

12:00 ML for Heavy Flavor Identification - Cameron Dean (MIT)

12:22 AI for Experimental Controls - Thomas Britton (JLAB)
13:00 Lunch (on your own)
Lunch (on your own)
13:00 - 14:00
Room: room 1019
14:00
14:00 - 17:00
Room: room 1019
Contributions

14:00 Tutorial on Graph Neural Networks - Yihui Ren (BNL)

14:50 Hackathon on October 14

15:30 Summaries

16:15 Future - Cristiano Fanelli (W&M)
Friday, 14 October 2022

09:00

09:00 - 17:00
Room: room 1019

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