Indico is back online after maintenance on Tuesday, April 30, 2024.
Please visit Jefferson Lab Event Policies and Guidance before planning your next event: https://www.jlab.org/conference_planning.

Login

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

    • 1
      Welcome
      Speaker: David Lawrence (Jefferson Lab)
      2021.07.26.AI_TownHall.pdf
      Google Slides
    • 2
      Deeply Learning deep inelastic scattering kinematics
      Speaker: Markus Diefenthaler (Jefferson Lab)
    • 3
      INTRA-ASTRA
      Speaker: Markus Diefenthaler (Jefferson Lab)
      Diefenthaler-AITownHall.pdf
    • 4
      InclAI
      Speaker: Gabriel Niculescu (JMU)
    • 5
      AI/ML for TRD
      Speaker: Yulia Furletova (JLAB)
      GEMTRD-ML_Jul2021.pptx
    • 6
      AI/ML on FPGA
      Speaker: Sergey Furletov (Jefferson Lab)
      MLFPGA_TownHall_2021_07_26.pdf
    • 7
      HYDRA
      Speaker: Thomas Britton (JLab)
      AITownHall_2021.pdf
    • 8
      Exp. Controls using AI
      Speaker: Torri Jeske (JLAB)
      AI_TownHall2021-5.pdf
    • 9
      Track ID for Clusters
      Speaker: Gagik Gavalian (Jefferson Lab)
    • 10
      De-noisingDrift Chamber using Autoencoders
      Speaker: Gagik Gavalian (Jefferson Lab)
    • 11
      CLAS12 Level-3 trigger
      Speaker: Gagik Gavalian (Jefferson Lab)
    • 12
      AI for reweighting MC di-hadron events for CLAS and CLAS12
      Speaker: Sebouh Paul (UC Riverside)
    • 13
      Training Classifiers with Autoencoder Event Generators in GlueX
      Speaker: Sean Dobbs (Florida State University)
    • 14
      μ/π/e PID
      Speaker: David Lawrence (Jefferson Lab)
    • 15
      Solving the inverse problem at the event level
      Speaker: Nobuo Sato (Jefferson Lab)
      JLab AI-town hall.pdf
    • 16
      Physics-Aware Generative Adversarial Networks for Physics Event Generation
    • 17
      Neural Network Simulation of Detector Efficiency
    • 18
      Unfolding of the CLAS detector with OmniFold
      Speaker: Luca Marsicano (INFN Genova)
    • 19
      ML to access multi-d xsection in hadron physics
      Speaker: Marco Battaglieri (JLab/INFNGE)
      ML-crosssect.pdf
    • 20
      Validation of 2 pion cross section extraction
      Speaker: Evgeny Isupov (Moscow State U.)
      validAItownHall.pdf
    • 21
      GAN uncertainty quantification in CLAS 2pi channel data analysis
    • 22
      Validation of event generators with extraction of physics observables
      Speaker: Astrid Hiller Blin (Jefferson Lab)
    • 23
      t-SNE analysis
    • 24
      Application of t-SNE analysis to 2 pion data analysis
      Speaker: Marco Battaglieri (JLab/INFNGE)
      ML-tSNE.pdf
    • 25
      SAUCER: a VAE-based generative algorithm
    • 26
      Detector Design Optimization for ECCE
      Speaker: Fanelli Cristiano (MIT)
      AI_Town_Hall_JLab_2021_Detector_Design_Optimization_for_ECCE.pdf
    • 27
      AIPWA
      Speaker: William Phelps (Christopher Newport University/Jefferson Lab)
      ai_townhall_pwa_07_26_21_phelps.pdf
    • 28
      Anomalous Resonance Identification with Autoencoders
      Speaker: William Phelps (Christopher Newport University/Jefferson Lab)
      Anomalous Resonance Identification with Autoencoders Andru Quiroga.pdf
    • 29
      Modeling Magnetic Fields with deep neural networks
      Speaker: William Phelps (Christopher Newport University/Jefferson Lab)
      Modeling Magnetic Fields with deep neural networks Andru Quiroga.pdf
    • 30
      Modern extraction of Compton Form Factors using global fits with artificial neural networks.
      Speaker: Zulkaida Akbar (University of Virginia)
      Zulkaida_AITownHall.pptx
    • 31
      Further constraints with simultaneous fitting in DVCS
      Speaker: Liliet Calero Diaz
    • 32
      Improvement in polarization measurements using artificial intelligence
    • 33
      Machine Learning for FCAL-II Shower Classification
    • 34
      A.I.-supported Research and Development of Novel Composite Aerogel Materials for Nuclear Physics Detectors
      Speaker: Fanelli Cristiano (MIT)
      AI_Town_Hall_JLab_07.26.2021_aerogel_UPL.pdf
Powered by Indico v3.3.2