I am a Research Scientist in the PRIME (Personalized Recommendations, Inference, Modeling, and Economics) group at Spotify. My research develops and applies methods at the intersection of causal inference, machine learning, and data-driven decision-making for problems in personalization, digitalization, and algorithmic fairness. At Spotify, I currently work on bandit experimentation, heterogeneous treatment effects, and off-policy evaluation in recommender systems.

I hold a PhD from the Department of Management, Technology, and Economics at ETH Zurich, supervised by Stefan Feuerriegel and Florian von Wangenheim. During my doctorate, I completed a research stay at the Operations, Information & Technology area at Stanford Graduate School of Business, hosted by Jann Spiess, interned as Machine Learning Research Scientist at Booking.com, and joined the non-profit AI startup Algorithm Audit as a contributor on statistical methodology. Previously, I obtained dual bachelor's and master's degrees in Statistics and Business & Economics from Lund University in Sweden and worked on causal inference and marketing science problems in market research, digital advertising, and educational technology.

Research Interests: Causal Inference; Statistical Machine Learning; Data-Driven Decision-Making; Econometrics; Personalization and Digitalization