Andrea, originally from Italy, graduated from the University of Manchester in 2018 with a First-Class Honours degree in Physics with Astrophysics. His Masters thesis involved the development of a deep learning pipeline for the identification of single pulse signals from pulsars in radio observations.
In summer 2021, Andrea worked as a research intern at the Institute of Astronomy, University of Cambridge. He created a Bayesian framework for ascribing the complexity in the X-ray spectrum of an AGN to either the partial-covering or the relativistic-reflection model.
Andrea joined LIV.INNO in 2022 as a PhD student in the Computational & Theoretical Galaxy Formation group at Liverpool John Moores University. His research focuses on the application of advanced machine learning techniques to the analysis of data from cosmological simulations to reconstruct the assembly history of galaxies and study the formation of structures in the cosmic web.
Andrea Sante
From the number and shapes of stellar streams one can reconstruct the accretion history of galaxies and constrain the nature of dark matter.