Simone Amoroso

Scientist @Deutsches Elektronen-Synchrotron DESY

Hamburg, DE

Coordinated a 50 people group to produce ten statistical analyses, and the optimisation of likelihood-free inference approaches to effective field theories. Performed a combination of W-boson mass measurements, through a thorough study of their common sources of systematic uncertainties and an estimate of their correlations. Optimised Deep Neural Network machine learning algorithms to reweight Monte Carlo simulation parameters, reducing by up to a factor of five the need for (expensive) simulated events in the collaboration. Developer of the xFitter open-source code for non-linear unconstrained optimizations. Leveraged the code to perform multiple phenomenological analyses reducing uncertainties in models of the proton structure by up to a factor of three. Delivered lecture courses on radiation detectors and particle physics at the University of Wuppertal.

University of Rome Tor Vergata

M.Sc, Particle Physics

University of Rome Tor Vergata

B.Sc, Physics