Machine learning for gravitational-wave data inference
Invited talk, Challenges and future perspectives in gravitational- wave astronomy: O4 and beyond, Lorentz Center, Leiden, Netherlands
Talks and presentations
Importance nested sampling with normalizing flows
Invited talk, 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Max-Planck-Institut für Plasmaphysik, Garching, Germany
Importance nested sampling with nessai for gravitational-wave inference
Talk, National Astronomy Meeting 2022, University of Warwick, UK
Importance nested sampling with nessai
Talk, BritGrav22, University of Glasgow (remote), UK
nessai: Improved nested sampling with normalising flows
Poster, National Astronomy Meeting 2021, University of Bath (remote), UK
Introduction to normalising flows
Invited talk, Machine Learning in Science, University of Glasgow, UK
Why is machine learning relevant to astronomers
Colloquium, Department of Physics, CSU Fullerton (remote), California, US
Incorporating machine learning into existing techniques for gravitational-wave inference: nested sampling with normalising flow
Invited talk, TianQin Research Center for Gravitational Physics, Sun Yat-sen University (remote), China
Incorporating machine learning into existing techniques for gravitational-wave inference: nested sampling with normalising flow
Invited talk, Max Planck Institute for Gravitational Physics, Albert Einstein Institute (remote), Germany
Convolutional Neural Networks for Gravitational Wave Detection
Poster, PCCP Workshop Series : Bayesian Deep Learning for Cosmology and Gravitational waves, Universite de Paris, Paris, France