About

I am an applied data scientist with a strong mathematical background, currently pursuing an M.Sc. in Mathematics. My work sits at the intersection of statistical modeling, machine learning, and real-world data.

I have hands-on experience building, evaluating, and iterating on machine-learning models, with a particular focus on NLP, time-series analysis, and anomaly detection.

I care deeply about model quality, interpretability, and robustness, and I try to make assumptions, limitations, and uncertainty explicit rather than implicit.

In my projects, I am especially interested in understanding when models can be trusted—and when they cannot.