Hi, my name is Joel (Swedish pronunciation "yo-el") and welcome to my website.

I am a Research Scientist in the Machine Learning and Economics group at Spotify. My research interests are in causal inference, machine learning, and data-driven decision-making, in particular for applications in marketing, healthcare, platforms, and related areas transformed by algorithms and digitalization. 

At Spotify, I work on methods for heterogeneous treatment effects, contextual bandits, and off-policy evaluation to optimize personalization and experimentation in large-scale 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 did a research visit to 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 AI startup Algorithm Audit as a contributor on statistical methodology, which I continue to be involved in. Previously, I obtained double bachelor's and master's degrees in Statistics and Business & Economics from Lund University, Sweden, and worked on marketing mix modeling at GfK Nielsen and as Digital Strategist at an award-winning European digital advertising agency.