First MODE Workshop on Differentiable Programming

Contribution List

1. Opening Lecture: Differentiable programming and design optimization

Atılım Güneş Baydin (University of Oxford)

06/09/2021, 13:55

Opening and Keynote Lectures

3. Keynote Lecture: JAX for scientific computing

Dr Adam Paszke (Google Brain)

06/09/2021, 14:55

Opening and Keynote Lectures

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

Opening and Keynote Lectures

4. Machine Learning application to volcano eruption forecasting using muography

Laszlo Olah (Hungarian Academy of Sciences (HU))

06/09/2021, 17:25

Applications in Muon Tomography

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

Applications in Muon Tomography

6. Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis

Patrick Stowell (University of Sheffield)

06/09/2021, 18:15

Applications in Muon Tomography

7. TomOpt: PyTorch-based Differential Muon Tomography Optimisation

Giles Chatham Strong (Universita e INFN, Padova (IT))

06/09/2021, 18:40

Applications in Muon Tomography

8. Optimization of LHCb calorimeter

Alexey Boldyrev (NRU Higher School of Economics (Moscow, Russia))

07/09/2021, 09:00

Applications in Particle Physics

11. CMS Open data application of INFERNO

Lukas Layer (Universita e INFN, Padova (IT))

07/09/2021, 09:25

Applications in Particle Physics

12. Automatic Differentiation for error analysis in lattice QCD

Alberto Ramos Martinez (Univ. of Valencia and CSIC (ES))

07/09/2021, 09:50

Applications in Particle Physics

10. Optimization of HEP accelerators

Auralee Linscott Edelen

07/09/2021, 10:15

Applications in Particle Physics

9. Generation of Calorimeter Showers with Normalizing Flows: CaloFlow

Claudius Krause (Rutgers University)

07/09/2021, 10:40

Applications in Particle Physics

14. DNN for distortion fluctuation calibration of ALICE TPC

Maja Kabus (Warsaw University of Technology (PL))

07/09/2021, 11:35

Applications in Nuclear Physics

15. Neural networks for jet-energy subtraction in heavy-ion collisions

Laura Brittany Havener (Yale University (US))

07/09/2021, 12:00

Applications in Nuclear Physics

18. Deep learning stochastic processes with QCD phase transition

Lingxiao Wang (Frankfurt Institute for Advanced Studies)

07/09/2021, 12:25

Applications in Nuclear Physics

17. Machine Learning Applications for CBM at FAIR

Shahid Khan (University of Tuebingen)

07/09/2021, 12:50

Applications in Nuclear Physics

16. In-medium heavy-quark interaction via deep learning

Shuzhe SHI (McGill University)

07/09/2021, 13:15

Applications in Nuclear Physics

19. Cosmology in the machine learning era

Francisco Villaescusa-Navarro (OATS-INAF)

07/09/2021, 14:30

Applications in AstroHEP

20. Gravitational Waves Detectors

Dr Mateusz Bawaj (Università degli Studi di Perugia)

07/09/2021, 14:55

Applications in AstroHEP

21. Optimization of Optical and Radio Detectors for high-energy Neutrinos

Christian Glaser (RWTH Aachen)

07/09/2021, 15:20

Applications in AstroHEP

22. Targeted dark matter substructure inference with differentiable strong lensing

Adam Coogan (University of Amsterdam)

07/09/2021, 15:45

Applications in AstroHEP

23. Cherenkov Telescopes

Gernot Maier

07/09/2021, 16:10

Applications in AstroHEP

24. AI In Neutrinoless Double Beta Decay Searches

Corey Adams (Argonne National Laboratory)

07/09/2021, 17:00

Applications in Neutrino Detection

25. Machine Learning Techniques for Water Cherenkov Event Reconstruction

Nick Prouse (Queen Mary University of London)

07/09/2021, 17:25

Applications in Neutrino Detection

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

Applications in Neutrino Detection

27. Dark matter and its ML-enhanced direct detection

Christopher Tunnell (Rice University)

07/09/2021, 18:15

Applications in Neutrino Detection

28. P-ONE: Test Case for ML Based Detector Design

Christian Haack (III. Physikalisches Institut B, RWTH Aachen University)

07/09/2021, 18:40

Applications in Neutrino Detection

35. GENETIS: Evolving Antennas for Ultra-High Energy Neutrino Detection

Alexander Machtay (Ohio State University)

07/09/2021, 19:05

Applications in Neutrino Detection

29. Gradient-based probabilistic inference in Pyro.ai

Dr Fritz Obermeyer (Broad Institute)

08/09/2021, 14:30

Progress in Computer Science

31. Efficient and modular implicit differentiation

Dr Mathieu Blondel (Google Brain)

08/09/2021, 14:55

Progress in Computer Science

32. Neos: upstream optimization for downstream inference

Nathan Daniel Simpson (Lund University (SE))

08/09/2021, 15:50

Progress in Computer Science

30. CERN GRID on Kubernetes

Lukas Alexander Heinrich (CERN)

08/09/2021, 16:15

Progress in Computer Science

33. Quantum Machine Learning

Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))

08/09/2021, 16:40

Progress in Computer Science

13. Quantum Machine Learning

Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))

Applications in Particle Physics