2nd g2net training school CA17137

Lecture Room 201 (University of Malta Valletta Campus)

Lecture Room 201

University of Malta Valletta Campus

St. Paul's Street, Valletta, VLT1216, Malta
Elena Cuoco

CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning


The breakthrough discovery of gravitational waves on September 14, 2015 was made possible through synergy of techniques drawing from expertise in physics, mathematics, information science and computing.  At present, there is a rapidly growing interest in Machine Learning (ML), Deep Learning (DL), classification problems, data mining and visualization and, in general, in the development of new techniques and algorithms for efficiently handling the complex and massive data sets found in what has been coined "Big Data", across a broad range of disciplines, ranging from Social Sciences to Natural Sciences. The rapid increase in computing power at our disposal and the development of innovative techniques for the rapid analysis of data will be vital to the exciting new field of Gravitational Wave (GW) Astronomy, on specific topics such as control and feedback systems for next-generation detectors, noise removal, data analysis and data-conditioning tools.The discovery of GW signals from colliding binary black holes (BBH) and the likely existence of a newly observable population of massive, stellar-origin black holes, has made the analysis of low-frequency GW data a crucial mission of GW science. The low-frequency performance of Earth-based GW detectors is largely influenced by the capability of handling ambient seismic noise suppression. This Cost Action aims at creating a broad network of scientists from four different areas of expertise, namely GW physics, Geophysics, Computing Science and Robotics, with a common goal of tackling challenges in data analysis and noise characterization for GW detectors.


Training School Objective

This training school is targeted towards scientists with expertise in Gravitational Wave Physics, Geophysics and Computing Science.

Lecture Modules:

  • Signal Processing,
  • Time Series Analysis,
  • Machine Learning,
  • Deep Learning,
  • Gravitational Waves,
  • Geophysics

Financial Support

Financial support is not available more.

  • Agata Trovato
  • Alberto Iess
  • Alejandro Torres Forne
  • Alessandro Staniscia
  • Ana Vranković
  • Anastasia Seifert
  • Andrea Chincarini
  • Andrei Utina
  • Andrew Miller
  • Antoine Depasse
  • Atousa Pournaghi
  • Carlo Giunchi
  • Christopher Zerafa
  • constantina nicolaou
  • Costantino Pacilio
  • Dario Jozinović
  • Denis Selimović
  • Eftim Zdravevski
  • Elena Cuoco
  • Emina Dzaferovic
  • Enrico Catalano
  • Fatih Özkaynak
  • Federico De Lillo
  • Filip Morawski
  • Francesca Badaracco
  • Francesco Di Renzo
  • Francesco Marangio
  • Franko Hržić
  • Gideon Koekoek
  • Goran Mitrov
  • Gregory Beroza
  • Grégory Baltus
  • Ingrid Vella
  • Ioannis Koutalios
  • Isabel Cordero-Carrión
  • Jade Powell
  • José Joaquín Moll Crespo
  • Kanita Karaduzovic-Hadziabdic
  • Krishna Khakurel
  • Luca Antiga
  • Luigia Petre
  • Mahmoud Elmasry
  • Mariah Zammit
  • Matteo Di Giovanni
  • Matthew Agius
  • Michael Gauci
  • Michal Bejger
  • Miquel Lluís Llorens Monteagudo
  • Miriam Rodríguez Sánchez
  • Mukharbek Organokov
  • Márcio Ferreira
  • Nunziato Sorrentino
  • Ondřej Zelenka
  • Ornella Juliana Piccinni
  • Osman Tayfun Bişkin
  • Rob Walet
  • Sebastiano Randino
  • Sonja Gaviano
  • Tomasz Bulik
  • Tomislav Andric
  • Umberto Giacomelli
  • Velimir Ilic
  • Vincent Boudart
  • Víctor Muñoz
Marta Budroni