I spent two years on a degree in China that called itself AI but was closer to EE. Then I got to Leiden, worked at Airbus and Lenovo on real industrial problems, spent a year at Tsinghua building speech-recognition models — and came away with the same conclusion from every direction: the interesting work isn't in the middle of the stack. It's at the intersection of data and decisions.
The bottleneck is never the model. It's the gap between what the data shows and what reaches the room where decisions happen. I'd rather close that gap than optimise one more loss function.
Starting MSc Management & Data Science at TU Munich in 2026. Interested in how data infrastructure meets business strategy — particularly in finance.
Outside of work I read annual reports for fun (a questionable hobby), follow macro markets, and think about how the same data pipeline problems I saw at Airbus show up in banking back offices. I also enjoy photography, cook decent Chinese food, and am slowly learning German for Munich.
Open to internships and conversations in applied ML, data engineering, fintech, or anything where data meets business decisions. If you work in banking or asset management and want to talk, I'd especially like to hear from you.