Stephen Green

Principal Research Fellow · University of Nottingham

I develop machine learning methods for gravitational-wave astronomy, supported by a UKRI Future Leaders Fellowship. Based at the Nottingham Centre of Gravity, my group develops simulation-based inference methods for gravitational-wave data analysis, from parameter estimation to population studies. I also work on black hole perturbation theory, motivated by waveform modelling and foundational questions in general relativity.

Previously I was at the Max Planck Institute for Gravitational Physics (AEI), the Perimeter Institute, and a CITA National Fellow at Guelph. I did my PhD at the University of Chicago with Robert Wald.

Nature · 2025

Real-time inference for binary neutron star mergers using machine learning

Led by Max Dax, this work demonstrates that neural posterior estimation can enable one-second inference for binary neutron star mergers—even before the merger occurs. Fast sky localization is crucial for searching for multimessenger counterparts.

Read article arXiv

Software

Dingo – Deep Inference for Gravitational-Wave Observations

Open-source simulation-based inference code for gravitational waves, developed for research and use by the LIGO-Virgo-KAGRA collaboration.

Code Trained Networks

View all →

stephen.green2@nottingham.ac.uk
+44 7360 609808

B25 Mathematical Sciences Building
School of Mathematical Sciences
University of Nottingham