Lecturers and Committee

Andrea Beschi | McKinsey

Andrea is an Engagement Manager at McKinsey & Company. After a PhD in Physics at the University of Milano - Bicocca, he worked in the fintech sector as a Data Scientist, specialising in the application of AI methodologies in banking and credit risk estimation. He joined McKinsey in 2021 in the ‘QuantumBlack, AI by McKinsey’ practice, where he leverages AI to bring long-lasting and sizeable impact to global leading financial institutions.

Riccardo Finotello | CEA Paris-Saclay

Riccardo is a research engineer at CEA Paris-Saclay, France. His research focuses on theoretical and computational aspects of data analysis and machine learning. In particular, he focuses on the development of computer vision methods for hyperspectral image analysis for materials science, based on physics-informed techniques and on quantifying the reliability of artificial intelligence models. He is also interested in the use of random matrix theory and the renormalization group for signal detection in complex regimes, where principal component analysis generally fails.

Stefano Giagu | Sapienza University of Rome

Stefano Giagu is a Professor of Physics at Sapienza University of Rome. His research focuses in high energy  and applied physics,  in the development of machine learning, deep learning, AI explainability, and quantum computation, with applications in different fields. He participated in the L3 experiments at CERN and CDF at Fermilab and is a member of the ATLAS collaboration at the Large Hadron Collider at CERN in Geneva, of which he has been national PI from 2019 to 2023. Since 2021 he is the coordinator of the international project MUCCA: Multi-disciplinary Use Cases for Convergent New Approaches to AI explainability.

Chris Moore | University of Cambridge

Chris Moore's interests include black holes, gravitational waves, and relativistic astrophysics, focusing on the role Einstein’s general relativity plays in shaping the observable universe. His research primarily centers on binary black hole systems and how the gravitational wave signals they emit can elucidate fundamental physics. Another central theme in his work is the advancement of novel Bayesian data analysis techniques for examining gravitational-wave time-series data. He has taught a variety of subjects in physics and astronomy, with his current teaching engagements primarily associated with the newly introduced MPhil in Data Intensive Science at Cambridge.

Guido Sanguinetti | International School for Advanced Studies (SISSA)

Guido leads the Machine Learning and Systems Biology group at SISSA. His main interests are in statistical modelling of biomedical data, with particular reference to Bayesian methodologies to interrogate sparse, high-dimensional data sets emerging from next generation sequencing experiments.