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First MODE Workshop on Differentiable Programming

6–8 Sept 2021
CP3, Louvain-la-Neuve
Europe/Zurich timezone

Contribution List

34 out of 34 displayed
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  1. 1. Opening Lecture: Differentiable programming and design optimization
    Atılım Güneş Baydin (University of Oxford)
    06/09/2021, 13:55
  2. 3. Keynote Lecture: JAX for scientific computing
    Dr Adam Paszke (Google Brain)
    06/09/2021, 14:55
  3. 2. Keynote Lecture: Generalization Properties of Deep Neural Networks Through The Prism of Interpolation
    Prof. Mikhail Belkin (Halicioğlu Data Science Institute, UCSD)
    06/09/2021, 15:55
  4. 4. Machine Learning application to volcano eruption forecasting using muography
    Laszlo Olah (Hungarian Academy of Sciences (HU))
    06/09/2021, 17:25
  5. 5. Role of AI in the industrial applications of muon tomography
    Pablo Martinez Ruiz Del Arbol (Universidad de Cantabria and CSIC (ES))
    06/09/2021, 17:50
  6. 6. Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis
    Patrick Stowell (University of Sheffield)
    06/09/2021, 18:15
  7. 7. TomOpt: PyTorch-based Differential Muon Tomography Optimisation
    Giles Chatham Strong (Universita e INFN, Padova (IT))
    06/09/2021, 18:40
  8. 8. Optimization of LHCb calorimeter
    Alexey Boldyrev (NRU Higher School of Economics (Moscow, Russia))
    07/09/2021, 09:00
  9. 11. CMS Open data application of INFERNO
    Lukas Layer (Universita e INFN, Padova (IT))
    07/09/2021, 09:25
  10. 12. Automatic Differentiation for error analysis in lattice QCD
    Alberto Ramos Martinez (Univ. of Valencia and CSIC (ES))
    07/09/2021, 09:50
  11. 10. Optimization of HEP accelerators
    Auralee Linscott Edelen
    07/09/2021, 10:15
  12. 9. Generation of Calorimeter Showers with Normalizing Flows: CaloFlow
    Claudius Krause (Rutgers University)
    07/09/2021, 10:40
  13. 14. DNN for distortion fluctuation calibration of ALICE TPC
    Maja Kabus (Warsaw University of Technology (PL))
    07/09/2021, 11:35
  14. 15. Neural networks for jet-energy subtraction in heavy-ion collisions
    Laura Brittany Havener (Yale University (US))
    07/09/2021, 12:00
  15. 18. Deep learning stochastic processes with QCD phase transition
    Lingxiao Wang (Frankfurt Institute for Advanced Studies)
    07/09/2021, 12:25
  16. 17. Machine Learning Applications for CBM at FAIR
    Shahid Khan (University of Tuebingen)
    07/09/2021, 12:50
  17. 16. In-medium heavy-quark interaction via deep learning
    Shuzhe SHI (McGill University)
    07/09/2021, 13:15
  18. 19. Cosmology in the machine learning era
    Francisco Villaescusa-Navarro (OATS-INAF)
    07/09/2021, 14:30
  19. 20. Gravitational Waves Detectors
    Dr Mateusz Bawaj (Università degli Studi di Perugia)
    07/09/2021, 14:55
  20. 21. Optimization of Optical and Radio Detectors for high-energy Neutrinos
    Christian Glaser (RWTH Aachen)
    07/09/2021, 15:20
  21. 22. Targeted dark matter substructure inference with differentiable strong lensing
    Adam Coogan (University of Amsterdam)
    07/09/2021, 15:45
  22. 23. Cherenkov Telescopes
    Gernot Maier
    07/09/2021, 16:10
  23. 24. AI In Neutrinoless Double Beta Decay Searches
    Corey Adams (Argonne National Laboratory)
    07/09/2021, 17:00
  24. 25. Machine Learning Techniques for Water Cherenkov Event Reconstruction
    Nick Prouse (Queen Mary University of London)
    07/09/2021, 17:25
  25. 26. Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for 3D Particle Imaging Detectors
    François Drielsma (Universite de Geneve (CH))
    07/09/2021, 17:50
  26. 27. Dark matter and its ML-enhanced direct detection
    Christopher Tunnell (Rice University)
    07/09/2021, 18:15
  27. 28. P-ONE: Test Case for ML Based Detector Design
    Christian Haack (III. Physikalisches Institut B, RWTH Aachen University)
    07/09/2021, 18:40
  28. 35. GENETIS: Evolving Antennas for Ultra-High Energy Neutrino Detection
    Alexander Machtay (Ohio State University)
    07/09/2021, 19:05
  29. 29. Gradient-based probabilistic inference in Pyro.ai
    Dr Fritz Obermeyer (Broad Institute)
    08/09/2021, 14:30
  30. 31. Efficient and modular implicit differentiation
    Dr Mathieu Blondel (Google Brain)
    08/09/2021, 14:55
  31. 32. Neos: upstream optimization for downstream inference
    Nathan Daniel Simpson (Lund University (SE))
    08/09/2021, 15:50
  32. 30. CERN GRID on Kubernetes
    Lukas Alexander Heinrich (CERN)
    08/09/2021, 16:15
  33. 33. Quantum Machine Learning
    Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))
    08/09/2021, 16:40
  34. 13. Quantum Machine Learning
    Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))