Data Never Lie

Research

Research and Academia

Gravitational-wave science, data analysis, and artificial intelligence for complex physical systems.

Publications ORCID

Research focus

  • Gravitational waves
  • Detector characterization
  • Noise analysis
  • Machine learning
  • Artificial intelligence
  • Signal detection

My research focuses on gravitational-wave data analysis, detector characterization, and the development of machine learning and AI methods for extracting weak astrophysical signals from complex and noisy data. I am a member of the LIGO/Virgo/KAGRA Collaboration.

Academic positions

University of Bologna

Full Professor
2024 – present

European Gravitational Observatory

Head of the Data Science Office
2018 – 2024

Scuola Normale Superiore

Associate Faculty
2018 – 2024

Leadership and coordination

  • Leader of the Virgo Noise Analysis Group (2008–2014)
  • Scientific Coordinator of the GraWIToN ITN (2014–2018)
  • Co-chair of the ML informal group (LIGO/Virgo)

Selected publications

Applications of machine learning in gravitational-wave research with current interferometric detectors

Living Review in Relativity (2025)

Read paper

Machine Learning for GW science

Machine Learning: Science and Technology (2021)

Read paper

Projects and networks

COST Action CA17137

A European network connecting gravitational-wave physics, geophysics, and machine learning.

COST page

GraWIToN ITN

Initial Training Network for early-career researchers in gravitational-wave science.

Cordis project website