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Comp F3 White Paper planning

    • 8:00 AM 8:05 AM
      Introduction/WP schedule 5m
    • 8:05 AM 8:10 AM
      WP update 5m
      Speaker: Brian Nord (Fermilab)
    • 8:10 AM 8:15 AM
      GNN/Instance Segmentation WPs 5m
      Speaker: Savannah Thais (Princeton University)
    • 8:15 AM 8:20 AM
      WP on Detector Simulation 5m
      Speaker: Mikuni Vinicius (LBNL)
    • 8:20 AM 8:25 AM
      Symmetry Group Equivariant Architectures 5m
      Speaker: Mariel Pettee (Yale University)
    • 8:30 AM 8:35 AM
      Data Science/ML education in HEP 5m
      Speaker: Mark Neubauer (University of Illinois at Urbana-Champaign)
    • 8:40 AM 8:45 AM
      Self-Driving System for Physics 5m
      Speaker: Cecilia Tosciri
    • 8:45 AM 8:50 AM
      Applications of Machine Learning to Lattice Quantum Field Theory 5m
      Speakers: Daniel Hackett (University of Colorado, Boulder), Daniel Hackett
    • 8:50 AM 8:55 AM
      Model Independent Searches for New Physics using Machine Learning 5m

      Our main idea is to assess the performance of different semi-supervised or unsupervised methods using different benchmark data sets. Besides commonly explored multi-jet signatures, we also want to consider multi-lepton processes. In order to compare the performance of different methods, we also consider several types of data input, e.g., kinematic variables and image data sets. This will provide a comparison of different methods on the same footing.

      Speaker: Charanjit Kaur
    • 8:55 AM 9:00 AM
      Community Needs, Tools and Resources 5m
      Speaker: Dylan Rankin (Massachusetts Institute of Technology)