In this website you will find posts both in Italian (my native language) and English. My intention is to talk about my work, my research, the signal processing, machine learning, but also about my passions such as travelling and photography, things I love doing in my spare time. I made all the photos you see in this site. Enjoy the site, have fun and share my posts if you liked them.
In questo sito web troverete articoli sia in Italiano (la mia lingua madre) che in Inglese. La mia intenzione è di parlare del mio lavoro, di analisi dei segnali, machine learning, ma anche delle mie passioni quali viaggiare e fare fotografie, cose che amo fare nel mio tempo libero. Infatti, le foto che vedrete nel blog, sono miei scatti. Divertitevi e condividete i miei posts, se vi sono piaciuti.
I'm Head of Data Science Office at European Gravitational Observatory, since March 2018 Associate Faculty at Scuola Normale Superiore. I've been working on Data Analysis for Virgo experiment since more than 20 years. After degree in Physics on Statistical Analysis in Astrophysics for Large Scale Structure of the Universe, I won a PhD fellowship at Physics Department in Pisa and started working for Virgo experiment. I'm expert in noise analysis and system identification, developing algorithms for data conditioning and signals detection. I leaded the noise analysis group for Virgo from 2008 to 2014, developing algorithms for data characterization and for the analysis of transient signals and working with machine learning techniques in this field. From 2014 to 2018 I was the Scientific Coordinator on an Initial Training Network GraWIToN, addressed to the training of young scientist in Gravitational Wave research.
I'm co-chair of the Machine Learning informal group in the LIGO/Virgo collaboration and Kaggle master. I was the main proposer for a COST action “A network for Gravitational Waves, Geophysics and Machine Learning”, which was financed by COST association. Now I'm Cost Action 17137 chair.
We're developing alternative ways to train our AI systems so that we can do more with less labeled training data overall. Learn how our “semi-weak supervision” method is delivering state-of-the-art performance for highly efficient, production-ready models. https://t.co/8tMN6M24LK
It's a big day @USCLONI! We just launched a new smartphone app, Schol-AR, that has the power to revolutionize how scientific data is shared. @TylerArdPhD built Schol-AR so researchers can embed data viz in papers, posters & more using AR. More here: https://t.co/aX621DtG9N @USC