Nikolaos Stergioulas

Nikolaos Stergioulas

Professor at the Aristotle University of Thessaloniki

Recent talks and presentations

FROM ALGORITHMS TO ASTROPHYSICS: Machine Learning in Gravitational Wave Astronomy

September 04, 2025

Plenary Talk, 21st Conference of HSRGC, Corfu, Greece

Machine Learning is finding diverse applications in the field of astrophysics and gravitational wave astronomy. This talk surveys our recent advancements in applying such methodologies. We demonstrate the utility of Deep Residual Networks (ResNets) in discovering new gravitational wave events from binary black hole mergers. Next, we present the calculation of the astrophysical detection probability in the framework of a machine-learning detection code. Furthermore, we discuss the application of deep neural networks in predicting the properties of neutron stars in an alternative theory of gravity and, finally, we present the application of normalizing flows within Preconditioned Monte Carlo methods to significantly accelerate Bayesian parameter estimation for binary neutron star post-merger signals.

LEVERAGING MACHINE LEARNING FOR GRAVITATIONAL WAVE DISCOVERIES: From Binary Black Hole Mergers to Probing High-Density Nuclear Matter

July 21, 2025

Plenary Talk, 11th Conference of the Polish Society on Relativity, Wroclaw, Poland

Machine Learning is finding diverse applications in the field of astrophysics and gravitational wave astronomy. This talk surveys our recent advancements in applying such methodologies. We demonstrate the utility of Deep Residual Networks (ResNets) in discovering new gravitational wave events from binary black hole mergers. Furthermore, we discuss the importance of using robust metrics for evaluating the sensitivity of detection pipelines and present the calculation of the astrophysical probability in the framework of a machine-learning detection code. Finally, we discuss the application of normalizing flows within Preconditioned Monte Carlo methods to significantly accelerate Bayesian parameter estimation for binary neutron star post-merger signals.

LEVERAGING MACHINE LEARNING FOR NEUTRON STAR PHYSICS: From Gravitational Waveform Modeling to Probing High-Density Nuclear Matter

June 26, 2025

Colloquium, Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, Utrecht, Netherlands

Machine Learning is finding diverse applications in the field of astrophysics and gravitational wave astronomy. This talk surveys our recent advancements in applying such methodologies. We demonstrate the utility of artificial neural networks (ANNs) in constructing surrogate models for frequency-domain post-merger gravitational wave spectra, showing higher fidelity than multilinear regression. In the time domain, we construct gravitational waveforms for the post-merger phase using K-nearest neighbor (KNN) regression techniques and discuss the training data requirements toward robust, EOS-agnostic models. Furthermore, we discuss the application of normalizing flows within Preconditioned Monte Carlo methods to significantly accelerate Bayesian parameter estimation for binary neutron star post-merger signals. The presentation will also detail the creation of ANN-driven surrogates for neutron star structural properties (mass, radius) within the framework of 4D Einstein-Gauss-Bonnet gravity. These tools yield dramatic computational speed-ups, thus critically enhancing the feasibility of complex Bayesian inference in modified gravity landscapes.