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First MODE Workshop on Differentiable Programming

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

Contribution List

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