Hi, and welcome to my website.

I am a Research Scientist at Spotify, since the start of 2024. My research lies primarily in causal inference, statistical machine learning, and data-driven decision-making, with applications in digital domains. At Spotify, I work on the use of contextual bandits, off-policy evaluation and heterogenous treatment effects for optimizing experimentation and personalization in recommender systems, most recently for improving training and evaluation of models and recommendations powered by AI.

I hold a PhD from the Department of Management, Technology, and Economics at ETH Zurich. My dissertation focused on the use of causal machine learning for decision-making in digital healthcare, digital marketing, and online platforms. My supervisors were Stefan Feuerriegel and Florian von Wangenheim. During my PhD, I visited the Operations, Information & Technology area at Stanford Graduate School of Business, hosted by Jann Spiess, and interned as Machine Learning Researcher at Booking.com in Amsterdam. I also joined Algorithm Audit as a technical contributor, which I continue to be involved in. 

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