I develop methods that turn complex data into meaningful insights, combining applied data analysis, statistics and machine learning to address questions with practical impact. As a postdoctoral researcher at the Epidemiology, Biostatistics and Prevention Institute (EBPI) at the University of Zurich, I apply large language models and statistical techniques to patient narratives, free-text responses and structured survey data from health research projects, including the Swiss Multiple Sclerosis Registry. My work explores how these complementary sources can help us understand patients’ experiences, identify patterns and predict future events in their disease journeys.

My broader research interests span computational linguistics, information retrieval and data-driven assessment. During my doctoral research, I investigated how machine learning and data science can help assess competencies across different contexts, using digital interactions to capture skills and real-world problem-solving. Across these areas, I am interested in connecting technical methods with meaningful questions and making the results useful beyond the analysis itself.

I have lived and worked in different countries, which has given me a global outlook and a broad perspective. Experiencing different cultures and ways of working has made me more adaptable, creative, and curious, always ready to take on new challenges and solve problems in different ways.

Outside of work, I love cooking, baking, and travelling. I enjoy cross-stitching, reading, and occasionally going for a hike. I am also into puzzles and board games, and on top of that, I collect ducks.