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JLab AI Town Hall

→ US/Eastern
David Lawrence (Jefferson Lab), Malachi Schram (Thomas Jefferson National Accelerator Facility)
Description

Live notes:

 https://docs.google.com/document/d/1ijlhohSAc9pLmCJwpMKKj4VJfYLEtMXG20WRM58W7eU/edit?usp=sharing

    • 2:00 PM → 2:10 PM
      Welcome 10m
      Speaker: David Lawrence (Jefferson Lab)
      2021.07.26.AI_TownHall.pdf
      Google Slides
    • 2:10 PM → 2:15 PM
      Deeply Learning deep inelastic scattering kinematics 5m
      Speaker: Markus Diefenthaler (Jefferson Lab)
    • 2:15 PM → 2:20 PM
      INTRA-ASTRA 5m
      Speaker: Markus Diefenthaler (Jefferson Lab)
      Diefenthaler-AITownHall.pdf
    • 2:20 PM → 2:25 PM
      InclAI 5m
      Speaker: Gabriel Niculescu (JMU)
    • 2:25 PM → 2:30 PM
      AI/ML for TRD 5m
      Speaker: Yulia Furletova (JLAB)
      GEMTRD-ML_Jul2021.pptx
    • 2:30 PM → 2:35 PM
      AI/ML on FPGA 5m
      Speaker: Sergey Furletov (Jefferson Lab)
      MLFPGA_TownHall_2021_07_26.pdf
    • 2:35 PM → 2:40 PM
      HYDRA 5m
      Speaker: Thomas Britton (JLab)
      AITownHall_2021.pdf
    • 2:40 PM → 2:45 PM
      Exp. Controls using AI 5m
      Speaker: Torri Jeske (JLAB)
      AI_TownHall2021-5.pdf
    • 2:45 PM → 2:50 PM
      Track ID for Clusters 5m
      Speaker: Gagik Gavalian (Jefferson Lab)
    • 2:50 PM → 2:55 PM
      De-noisingDrift Chamber using Autoencoders 5m
      Speaker: Gagik Gavalian (Jefferson Lab)
    • 2:55 PM → 3:00 PM
      CLAS12 Level-3 trigger 5m
      Speaker: Gagik Gavalian (Jefferson Lab)
    • 3:00 PM → 3:05 PM
      AI for reweighting MC di-hadron events for CLAS and CLAS12 5m
      Speaker: Sebouh Paul (UC Riverside)
    • 3:05 PM → 3:10 PM
      Training Classifiers with Autoencoder Event Generators in GlueX 5m
      Speaker: Sean Dobbs (Florida State University)
    • 3:10 PM → 3:15 PM
      μ/π/e PID 5m
      Speaker: David Lawrence (Jefferson Lab)
    • 3:15 PM → 3:20 PM
      Solving the inverse problem at the event level 5m
      Speaker: Nobuo Sato (Jefferson Lab)
      JLab AI-town hall.pdf
    • 3:20 PM → 3:25 PM
      Physics-Aware Generative Adversarial Networks for Physics Event Generation 5m
    • 3:25 PM → 3:30 PM
      Neural Network Simulation of Detector Efficiency 5m
    • 3:30 PM → 3:35 PM
      Unfolding of the CLAS detector with OmniFold 5m
      Speaker: Luca Marsicano (INFN Genova)
    • 3:35 PM → 3:40 PM
      ML to access multi-d xsection in hadron physics 5m
      Speaker: Marco Battaglieri (JLab/INFNGE)
      ML-crosssect.pdf
    • 3:40 PM → 3:45 PM
      Validation of 2 pion cross section extraction 5m
      Speaker: Evgeny Isupov (Moscow State U.)
      validAItownHall.pdf
    • 3:45 PM → 3:50 PM
      GAN uncertainty quantification in CLAS 2pi channel data analysis 5m
    • 3:50 PM → 3:55 PM
      Validation of event generators with extraction of physics observables 5m
      Speaker: Astrid Hiller Blin (Jefferson Lab)
    • 3:55 PM → 4:00 PM
      t-SNE analysis 5m
    • 4:00 PM → 4:05 PM
      Application of t-SNE analysis to 2 pion data analysis 5m
      Speaker: Marco Battaglieri (JLab/INFNGE)
      ML-tSNE.pdf
    • 4:05 PM → 4:10 PM
      SAUCER: a VAE-based generative algorithm 5m
    • 4:10 PM → 4:15 PM
      Detector Design Optimization for ECCE 5m
      Speaker: Fanelli Cristiano (MIT)
      AI_Town_Hall_JLab_2021_Detector_Design_Optimization_for_ECCE.pdf
    • 4:15 PM → 4:20 PM
      AIPWA 5m
      Speaker: William Phelps (Christopher Newport University/Jefferson Lab)
      ai_townhall_pwa_07_26_21_phelps.pdf
    • 4:20 PM → 4:25 PM
      Anomalous Resonance Identification with Autoencoders 5m
      Speaker: William Phelps (Christopher Newport University/Jefferson Lab)
      Anomalous Resonance Identification with Autoencoders Andru Quiroga.pdf
    • 4:25 PM → 4:30 PM
      Modeling Magnetic Fields with deep neural networks 5m
      Speaker: William Phelps (Christopher Newport University/Jefferson Lab)
      Modeling Magnetic Fields with deep neural networks Andru Quiroga.pdf
    • 4:30 PM → 4:35 PM
      Modern extraction of Compton Form Factors using global fits with artificial neural networks. 5m
      Speaker: Zulkaida Akbar (University of Virginia)
      Zulkaida_AITownHall.pptx
    • 4:35 PM → 4:40 PM
      Further constraints with simultaneous fitting in DVCS 5m
      Speaker: Liliet Calero Diaz
    • 4:40 PM → 4:45 PM
      Improvement in polarization measurements using artificial intelligence 5m
    • 4:45 PM → 4:50 PM
      Machine Learning for FCAL-II Shower Classification 5m
    • 4:50 PM → 4:55 PM
      A.I.-supported Research and Development of Novel Composite Aerogel Materials for Nuclear Physics Detectors 5m
      Speaker: Fanelli Cristiano (MIT)
      AI_Town_Hall_JLab_07.26.2021_aerogel_UPL.pdf
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