Summary
Giulia De Bonis is a Rome-based researcher at INFN with eight years of experience spanning neuroscience-inspired artificial intelligence and astroparticle physics. She combines data analysis, machine-learning algorithms, and Python to advance bio-inspired cortical simulations and to develop acoustic detection and trigger algorithms for underwater neutrino telescopes. Her work includes Monte Carlo simulations, neutrino detection in submarine apparatuses, and shower reconstruction, bridging biological-inspired computation with high-energy physics instrumentation. She earned a PhD in Physics from Sapienza University of Rome with cum laude honors and has held multiple research roles at INFN and partner institutions. Based on her interdisciplinary trajectory, she uniquely positions teams to translate complex neural-inspired models into robust physics applications.
9 years of coding experience