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Q&A padlets

Links to the recordings will also be posted here.

29 July

SpeakerTalk TitleSession Time (BST)
  Introduction 09:00
Ed Porter DeepHMC: a machine learning based HMC algorithm for gravitational wave parameter
Stephen Green  Real-time gravitational-wave science with neural posterior estimation  09:35
Chayan Chatterjee  Denoising and Localization of Gravitational Wave Events Using Deep Learning  09:55
  Break 10:15
Maite Mateu-Lucena Machine learning applications to modelling the performance of parameter estimation samplers and compact binary waveforms   10:30
Michael Williams   Nessai: Improved nested sampling with normalising flows for gravitational-wave inference  10:50
  Break 11:10
  Discussion  11:25
  Lunch break  11:55
  Introduction  14:00
Szabolcs Marka  Why AI?  14:15
Cyril Cano  Fast and accurate gravitational-wave modelling with principal component regression  14:35
Marlin Schäfer  Simple Two-Detector Networks with Fast Background Estimation  14:55
  Break  15:15
Michael Puerrer  Modeling precessing binary black hole waveforms with machine learning  15:30
Paraskevi Nousi  Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling  15:50
Rodrigo Tenorio  Time-frequency track distance for comparing continuous gravitational wave signals 16:10
  Break 16:30
  Discussion 16:45
  End  17:15

30 July

SpeakerTalk TitleSession Time (BST)
  Introduction 09:00
SangHoon Oh A deep learning model of merger and ringdown waveform of binary blackhole coalescence 09:15
Kyungmin Kim Identification of Lensed Gravitational Waves with Deep Learning 09:35
Srashti Goyal Rapid identification of strongly lensed signals with machine learning 09:55
  Break 10:15
Shreejit Jadhav Improving significance of binary black hole mergers in Advanced LIGO data using deep learning: Confirmation of GW151216 10:30
Leïla Haegel Predicting the properties of black holes merger remnants with Deep Neural Networks 10:50
Matthew Mould Deep learning techniques to enhance gravitational-wave population inference 11:10
  Break 11:30
  Discussion 11:45
  Lunch break 12:15
  Introduction 14:00
Agata Trovato Neural networks for gravitational-wave trigger selection in single-detector periods 14:15
Filip Morawski Anomaly detection in the gravitational wave detectors data 14:35
Vincent Boudart ALBUS : Anomaly detector for Long duration BUrst Searches 14:55
  Break 15:15
Ryan Quitzow-James NNETFIX: An artificial neural network-based denoising engine for gravitational-wave signals 15:30
Piotr Gawron Deep learning and MLOps methods for gravitational waves characterization. 15:50
Tanmaya Mishra Optimization of model independent gravitational wave search using machine learning 16:10
  Break 16:30
  Discussion 16:45
  End 17:15