Lorenzo Porcaro

Lorenzo Porcaro is a Marie Skłodowska-Curie Postdoctoral Fellow at the Department of Computer, Control, and Management Engineering (DIAG) at Sapienza University of Rome, where he leads the project Algorithmic Auditing for Music Discoverability (AA4MD) in collaboration with Professor Tiziana Catarci of the HCI group and Professor Fabrizio Silvestri of the RSTLess research group. He completed his PhD cum laude at Universitat Pompeu Fabra (UPF) in Barcelona, with research conducted at the Music Technology Group, Department of Information and Communication Technologies. Prior to his postdoctoral role, he served as a Scientific Project Officer at the European Commission’s Joint Research Centre (JRC), contributing to the Human Behaviour and Machine Intelligence (HUMAINT) team and the European Centre for Algorithmic Transparency (ECAT).

Education:

• Bachelor's in Applied Mathematics, Sapienza University of Rome (2014)
• Master's in Sound and Music Computing, Universitat Pompeu Fabra (2015)
• Master's in Intelligent Interactive Systems, Universitat Pompeu Fabra (2018)
• PhD in Information and Communication Technologies, Universitat Pompeu Fabra (2022, cum laude)

His research focuses on recommender systems, algorithmic auditing, music information retrieval, and AI ethics. He investigates how algorithmic systems impact user behavior, diversity, and fairness in music discovery, with a strong emphasis on transparency and user empowerment. His work integrates technical AI methods with human-centered evaluation and societal impact analysis, particularly in the context of digital platform regulation such as the Digital Services Act (DSA). He has contributed to EU-funded projects like TROMPA and MusicalAI, and his publications appear in top venues including ACM Transactions on Recommender Systems, IEEE Transactions on Affective Computing, and RecSys.

His recent publications highlight a consistent focus on diversity, fairness, and auditing in music and information systems. Themes include longitudinal studies on music recommendation diversity, user-centric algorithmic auditing frameworks, open datasets for emotion recognition, and critical analyses of diversity in AI research communities. His work bridges technical innovation with ethical and societal considerations.

Scientific Awards and Recognition:

• Marie Skłodowska-Curie Postdoctoral Fellowship
• PhD awarded cum laude (with full marks)
• 2024 Featured Article by the Editor-in-Chief of ACM Transactions on Recommender Systems

Lorenzo actively mentors prospective PhD students and is involved in research leadership through organizing workshops such as MuRS (Music Recommender Systems) and HCMIR. He has no formal advisees listed yet but welcomes PhD applicants interested in music recommender systems and algorithmic auditing. His research is supported by significant grants, including the Marie Curie fellowship and prior EU and national funding. He is a member of the RSTLess research group and collaborates with leading experts in HCI and AI.

Labs and Research Groups:

• Principal Investigator of AA4MD project, Sapienza University of Rome
• Member of the RSTLess research group (led by Prof. Fabrizio Silvestri)
• Former member of the HUMAINT team at the European Commission’s JRC
• Collaborator with the Music Technology Group (MTG) at UPF
• Collaborator with the HCI group at DIAG, Sapienza