I am a Research Scientist in the Machine Learning and Economics lab at Spotify. My current work focuses on improving personalization and evaluation in recommender systems powered by machine learning and AI, by developing methods based on experimental design, statistical decision theory, econometrics and bandits/reinforcement learning. More broadly, my research interests are in statistical machine learning, causal inference and data-driven decision-making, including applications in marketing, online platforms, healthcare, and public policy.

I received my PhD from the Department of Management, Technology, and Economics at ETH Zurich in 2024, advised by Stefan Feuerriegel and Florian von Wangenheim. During my doctorate, I visited the Operations, Information & Technology area at Stanford Graduate School of Business, hosted by Jann Spiess, and interned as Machine Learning Research Scientist at Booking.com in Amsterdam. I also joined the non-profit organization Algorithm Audit, where I contribute to open-source software for bias detection in machine learning systems.

I hold double bachelors and masters degrees in Statistics and Business & Economics from Lund University, Sweden, and previously worked as Marketing Scientist at GfK (now part of Nielsen) and as analyst at an award-winning digital marketing agency.