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Resume (English only)
Academic Achievements
Recent publications include: Protocol to explain support vector machine predictions via exact Shapley value computation (STAR protocols, 2024); Learning characteristics of graph neural networks predicting protein–ligand affinities (Nature Machine Intelligence, 2023); Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel (Sci Rep, 2023); XGDAG: eXplainable Gene–Disease Associations via Graph Neural Networks (Bioinformatics, 2023). Received multiple grants and awards, such as the 2024 Lamarr Stipendium Program Fellowship, 2023 Fondi di Avvio alla Ricerca Tipo 1, 2022 Fondi di Avvio alla Ricerca Tipo 2, etc.
Research Experience
Postdoctoral Researcher at the Lamarr Institute and the Life Science Informatics and Data Science Department at the University of Bonn; Postdoctoral Project Researcher at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome.
Education
Ph.D. in Data Science, Sapienza University of Rome, Summa cum Laude, Doctor Europaeus; Visiting Ph.D. researcher at the University of Bonn (Germany); MSc in Engineering in Computer Science, Sapienza University of Rome, 110/110, Summa cum Laude, Excellent Graduate A.Y. 2018/19, Honours Programme, Erasmus Programme at Polytechnic University of Catalonia (Barcelona, Spain); BSc in Computer and System Engineering, Sapienza University of Rome, 110/110, Summa cum Laude, Honours Programme; High School Degree, Liceo Scientifico A. Labriola, 100/100, Summa cum Laude.
Background
Computer Engineer and Ph.D. in Data Science, with research interests in AI for medicine, focusing on deep learning and XAI in bioinformatics and chemoinformatics.