Department of Computer, Control and Management Engineering

Lorenzo Porcaro received his bachelor’s degree in Applied Mathematics in 2014 from the Sapienza - University of Rome (Italy) and his master’s degrees in Sound and Music Computing (2015) and Intelligent Interactive Systems (2018) from Universitat Pompeu Fabra (UPF), Barcelona (Spain). From 2018 to 2022, he pursued a PhD titled “Assessing the Impact of Music Recommendation Diversity on Listeners” at the Music Technology Group, part of the Department of Information and Communication Technology at UPF, under the supervision of Prof. Emilia Gómez and Prof. Carlos Castillo. His thesis focused on exploring new methods to assess the impact of music recommendation diversity on listeners’ behaviors and attitudes, providing empirical evidence of the role diversity plays in mediating the relationship between music recommendations and listeners. He was awarded his doctorate from UPF with the highest honors ("cum laude") in 2022.

During his PhD, Lorenzo was part of the TROMPA Project (Towards Richer Online Music Public-domain Archives), an international research initiative funded by the European Union to enhance the accessibility of public-domain digital music resources. He also contributed to the MusicalAI project, sponsored by the Ministry of Science and Innovation of the Spanish Government, where he explored AI applications for supporting musical experiences through a data-driven, human-centered approach. Before embarking on his PhD, he gained experience in the music industry in various data-engineering roles. In 2015, he interned at SoundCloud, one of the world’s largest music streaming platforms. Subsequently, he worked at MonkingMe, a Catalan startup developing a local music streaming service, and at BMAT, a company that monitors and reports global music usage across TVs, radios, venues, and digital platforms.

After the PhD, Lorenzo served as a Scientific Project Officer at the European Commission’s Joint Research Centre (JRC), where he was a member of the Human Behaviour and Machine Intelligence (HUMAINT) team within the Algorithmic Transparency Unit. In this role, he conducted research on the trustworthiness of recommender systems in the context of the Digital Service Act (DSA) and contributed to the European Centre for Algorithmic Transparency (ECAT). His research during this period focused on developing methods for auditing recommender systems.

Currently, Lorenzo is a Marie Skłodowska-Curie Postdoctoral Fellow at the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome (Italy). He is the Principal Investigator of the project titled Algorithmic Auditing for Music Discoverability (AA4MD) in collaboration with Prof. Tiziana Catarci, head of the HCI group at DIAG.