Links for meeting
The Zoom link is:
https://uofglasgow.zoom.us/j/93085388382?pwd=dFpOTWFzaHc5R2diZWNjN2dKNU1tdz09
Q&A padlets
Links to the recordings will also be posted here.
29 July
| Speaker | Talk Title | Session Time (BST) |
|---|---|---|
| Introduction | 09:00 | |
| Ed Porter | DeepHMC: a machine learning based HMC algorithm for gravitational wave parameter estimation |
09:15 |
| 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
| Speaker | Talk Title | Session 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 |