Go to the Indico Home Page Oldest event Older event Up to category Newer event Newest event
  • Indico style
  • Indico style - inline minutes
  • Indico style - numbered
  • Indico style - numbered + minutes
  • Indico Weeks View
    • Switch to the management area of this event
    Login

    2nd workshop on Artificial Intelligence for the Electron Ion Collider

    10–14 Oct 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 out of 45 displayed
    Export to PDF
    1. 45. EIC schedule and overview
      Elke-Caroline Aschenauer (BNL), Rolf Ent (Jefferson Lab)
      10/10/2022, 10:25
    2. 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
    3. 42. NSF perspective on opportunities for AI in nuclear physics
      Bogdan Mihaila (National Science Foundation)
      10/10/2022, 13:30
    4. 1. Tutorial on Ax/Botorch: Multi-Objective Optimization
      Max Balandat (Meta/AI Facebook )
      10/10/2022, 14:00
    5. 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
    6. 5. AI-driven detector design for the EIC
      Benjamin Nachman
      10/10/2022, 15:20
    7. 3. ML application for beam optics control in the LHC
      Elena Fol (CERN)
      10/10/2022, 16:00

      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...

      Go to contribution page
    8. 4. AI/ML overview for accelerator design activities
      Todd Satogata (Jefferson Lab)
      10/10/2022, 16:25
    9. 6. ML for QCD Analysis - 3D Imaging
      simonetta liuti (university of virginia)
      11/10/2022, 10:00
    10. 7. Modeling Hadronization Using ML and the Lund String Model
      Tony Menzo (U. Cincinnati)
      11/10/2022, 10:25
    11. 8. Modeling Hadronization Using ML and the Cluster Model
      Siodmok Andzrej (Jagiellonian U.)
      11/10/2022, 10:50
    12. 9. A(I)DAPT
      Astrid Hiller Blin (Thomas Jefferson National Accelerator Facility)
      11/10/2022, 11:30
    13. 10. Femtoscale Imaging of Nuclei using ML and Exascale Platforms
      nobuo sato (Jefferson Lab)
      11/10/2022, 11:55
    14. 11. Differentiable Simulations
      Benjamin Nachman
      11/10/2022, 12:20
    15. 12. Discussion
      11/10/2022, 12:45
    16. 15. Fast Detector Simulations with Machine Learning
      David Shih (Rutgers)
      11/10/2022, 14:00
    17. 14. Machine Learning in Spectroscopy and Partial Wave Analysis
      William Phelps (Christopher Newport University/Jefferson Lab)
      11/10/2022, 14:25
    18. 13. Reconstructing DIS and SIDIS properties
      Connor Pecar (Duke University)
      11/10/2022, 14:50
    19. 16. overview talk on ML-based unfolding
      Anja Butter (Heidelberg U.)
      11/10/2022, 15:35
    20. 17. Tutorial on Unfolding
      Fernando Torales - Acosta, Vinicius Mikuni (Other@lbl.gov;Staff@lbl.gov;Member@lbl.gov)
      11/10/2022, 16:00
    21. 18. Interpretable Networks for Identifying Leptons
      Daniel Whiteson (UC Irvine)
      12/10/2022, 10:00
    22. 23. Tagging heavy flavor jets @ RHIC
      Raghav Kunnawalkam Elayavalli (Vanderbilt University)
      12/10/2022, 10:30
    23. 20. Muon Identification with Deep Learning at EIC
      William Phelps (Christopher Newport University/Jefferson Lab)
      12/10/2022, 11:00
    24. 19. Machine Learning in ACTS
      Corentin Allaire (IJCLAB)
      12/10/2022, 11:28
    25. 21. ML particle identification with measured shower profiles from calorimetry
      Chao Peng (Argonne National Laboratory)
      12/10/2022, 11:48
    26. 22. Lambda event tagging at CLAS12
      Matthew McEneaney (Duke)
      12/10/2022, 12:06
    27. 43. ML for calorimetry
      Nathan Branson (Messiah U.)
      12/10/2022, 12:24
    28. 44. Data-driven learning: Flux+Mutability
      James Giroux (U. Regina)
      12/10/2022, 12:42
    29. 24. Tutorial on MLFlow
      Kishansingh Rajput (Jefferson Lab)
      12/10/2022, 14:00
    30. 25. Foundation Model Infrastructure
      Svitlana Volkova
      12/10/2022, 15:00
    31. 26. AI/ML hardware co-design
      Frank Liu (ORNL)
      12/10/2022, 15:18
    32. 27. Machine Learning with FPGA
      Nhan Tran (Fermilab)
      12/10/2022, 15:35
    33. 28. AI/Ml with HPC
      Joo Balint (ORNL)
      12/10/2022, 15:52
    34. 29. Panel Discussion
      Jin Huang (Brookhaven National Lab), Mike Williams (MIT), Tia Miceli (Fermilab)
      12/10/2022, 16:15
    35. 30. INDRA-ASTRA
      Markus Diefenthaler (Jefferson Lab)
      13/10/2022, 10:00
    36. 31. Fast ML for FPGA
      Sergey Furletov (JLab)
      13/10/2022, 10:22
    37. 32. AI for streaming readout: an architectural perspective
      Roberto Ammendola (INFN/Roma2)
      13/10/2022, 10:44
    38. 33. AI-based data reduction for streaming DAQ
      Jin Huang (Brookhaven National Lab)
      13/10/2022, 11:16
    39. 34. Streaming Readout for Next Generation Electron Scattering Experiments
      Mariangela Bondi (INFN/Catania)
      13/10/2022, 11:38
    40. 35. ML for Heavy Flavor Identification
      Cameron Dean (MIT)
      13/10/2022, 12:00
    41. 36. AI for Experimental Controls
      Thomas Britton (JLAB)
      13/10/2022, 12:22
    42. 37. Tutorial on Graph Neural Networks
      Yihui Ren (BNL)
      13/10/2022, 14:00
    43. 38. Hackathon on October 14
      13/10/2022, 14:50
    44. 39. Summaries
      13/10/2022, 15:30
    45. 40. Future
      Cristiano Fanelli (W&M)
      13/10/2022, 16:15