First MODE Workshop on Differentiable Programming

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Sessions*

Registration and COVID-safe QR code check

Opening and Keynote Lectures

Applications in AstroHEP

Organization of Future Activities and Closing of the Workshop

Progress in Computer Science

Brainstorming, expressions of interest, definition of tasks and goals, whitepaper

Applications in Nuclear Physics

Applications in Neutrino Detection

Applications in Particle Physics

Applications in Muon Tomography

Introduction and goals of the Workshop

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Contributions*

AI In Neutrinoless Double Beta Decay Searches (in session "Applications in Neutrino Detection")

Automatic Differentiation for error analysis in lattice QCD (in session "Applications in Particle Physics")

CERN GRID on Kubernetes (in session "Progress in Computer Science")

Cherenkov Telescopes (in session "Applications in AstroHEP")

CMS Open data application of INFERNO (in session "Applications in Particle Physics")

Cosmology in the machine learning era (in session "Applications in AstroHEP")

Dark matter and its ML-enhanced direct detection (in session "Applications in Neutrino Detection")

Deep learning stochastic processes with QCD phase transition (in session "Applications in Nuclear Physics")

DNN for distortion fluctuation calibration of ALICE TPC (in session "Applications in Nuclear Physics")

Efficient and modular implicit differentiation (in session "Progress in Computer Science")

Generation of Calorimeter Showers with Normalizing Flows: CaloFlow (in session "Applications in Particle Physics")

GENETIS: Evolving Antennas for Ultra-High Energy Neutrino Detection (in session "Applications in Neutrino Detection")

Gradient-based probabilistic inference in Pyro.ai (in session "Progress in Computer Science")

Gravitational Waves Detectors (in session "Applications in AstroHEP")

In-medium heavy-quark interaction via deep learning (in session "Applications in Nuclear Physics")

Keynote Lecture: Generalization Properties of Deep Neural Networks Through The Prism of Interpolation (in session "Opening and Keynote Lectures")

Keynote Lecture: JAX for scientific computing (in session "Opening and Keynote Lectures")

Machine Learning application to volcano eruption forecasting using muography (in session "Applications in Muon Tomography")

Machine Learning Applications for CBM at FAIR (in session "Applications in Nuclear Physics")

Machine Learning Techniques for Water Cherenkov Event Reconstruction (in session "Applications in Neutrino Detection")

Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis (in session "Applications in Muon Tomography")

Neos: upstream optimization for downstream inference (in session "Progress in Computer Science")

Neural networks for jet-energy subtraction in heavy-ion collisions (in session "Applications in Nuclear Physics")

Opening Lecture: Differentiable programming and design optimization (in session "Opening and Keynote Lectures")

Optimization of HEP accelerators (in session "Applications in Particle Physics")

Optimization of LHCb calorimeter (in session "Applications in Particle Physics")

Optimization of Optical and Radio Detectors for high-energy Neutrinos (in session "Applications in AstroHEP")

P-ONE: Test Case for ML Based Detector Design (in session "Applications in Neutrino Detection")

Quantum Machine Learning (in session "Progress in Computer Science")

Role of AI in the industrial applications of muon tomography (in session "Applications in Muon Tomography")

Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for 3D Particle Imaging Detectors (in session "Applications in Neutrino Detection")

Targeted dark matter substructure inference with differentiable strong lensing (in session "Applications in AstroHEP")

TomOpt: PyTorch-based Differential Muon Tomography Optimisation (in session "Applications in Muon Tomography")

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Events scheduled on*

06/09/2021

07/09/2021

08/09/2021

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