Biography

Lorenzo Perini is a research scientist in the Central Applied Science (CAS) team at Meta focusing on probabilistic machine learning. He earned his BSc in Mathematics from the University of Florence in 2017, and his MSc in Mathematical Engineering from Politecnico di Torino in 2019, where he specialized in data statistics and network optimization. His Master’s thesis, conducted in collaboration with Tierra S.p.A., explored predictive maintenance using Hidden Markov Models and Autoencoders.

In 2019, Lorenzo began his PhD in the DTAI Lab at KU Leuven under Prof. Dr. Jesse Davis, focusing on uncertainty quantification and anomaly detection. His work has been recognized with several fellowships, including a PhD fellowship from the Research Foundation – Flanders (FWO) and the Scientific Prize Gustave Boël-Sofina Fellowship for talented researchers for a long stay abroad. During his PhD, he was a visiting researcher at the University of Helsinki and completed an internship at Bosch Center for Artificial Intelligence (BCAI).

Lorenzo has published papers in prestigious conferences such as NeurIPS, ICML, KDD, AAAI, IJCAI, and ECAI. His main research interests include Uncertainty Quantification and Anomaly Detection, often with the introduction of the human-in-the-loop, e.g. via Active Learning and Learning to Reject. In March 2024, he defended his doctoral dissertation titled operational, uncertainty-aware, and reliable anomaly detection.