I am a researcher in artificial intelligence and music, currently working as a Research Scientist at Google DeepMind in San Francisco. The goal of my research is to build AI models for music co-creation by bringing machines closer to human-like audio understanding and developing new methods for controllable generation.
Previously, I was a PhD researcher at the UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM), working in collaboration with Universal Music Group. During this time, I focused on developing multimodal deep learning methods to enable automatic music understanding by learning richer representations from language and audio. Throughout my PhD, I have also spent time as a Student Researcher with the Magenta team at Google DeepMind and completed research internships at Adobe in San Francisco, Sony R&D in Tokyo and The Alan Turing Institute in London (as an Engage student). Prior to my PhD, I worked as a data scientist and machine learning engineer, and obtained an MSci in Physics from Imperial College London. For my master, I specialised in complex systems and computational physics, with a thesis on spatial scaling in human mobility models.
When I take off my researcher hat, I enjoy doing all things related to electronic music: I DJ, produce, host a monthly radio show focussed on experimental dance music, and co-run Co-Select and Sisu, two collectives promoting a balanced gender representation in the dance music scene.
I also co-founded the Italian Music Tech Meetup, a community for researchers, artists and makers with a shared passion for music technology.