I direct Spotify’s algorithmic impact & responsibility effort.
My research background revolves around the human side of data and machine learning. I am particularly interested in the impact that teams’ design, data and metrics decisions have on algorithmic outcomes. This includes the feedback loop between products and their users, the gap between people’s experiences and machines’ data interpretation, and recommendations’ social and cultural impact. This especially to music and podcasting.
I combine quantitative, large-scale data approaches with in-depth, qualitative research to understand both what is happening and why. This also includes translating abstract calls to action into concrete data-informed policy, tooling and pragmatic business and product direction.
My work has spanned voice and conversational platforms, quality of personalized recommendations, ad moderation, location data interpretation, and human-robot interaction. I hold multiple patents, mostly related to leading data and research for Spotify’s voice platform, and have been lucky enough to publish and speak at top research venues.