I am a postdoctoral researcher with a Ph.D. in Social Data Science from the University of Copenhagen, Denmark. I currently work at Aarhus University on AI use in education, with a focus on how generative AI is reshaping academic writing, learning, and evaluation. I am also affiliated with the The Center for Social Data Science (SODAS) at the University of Copenhagen.

My research sits at the intersection of computational social science, political science, machine learning, natural language processing, and network science. I study how patterns of interaction, attention, selection, and participation shape broader social structures. Across my work, I use large-scale and often unstructured data, including text, images, social media traces, and observational data, to study social and political behavior.

I am especially interested in how computational methods can help us understand complex social phenomena. My work has examined political participation and responsiveness on social media, discursive communities, ethnographic fieldnotes, cultural change, beauty standards, and the use of generative AI in education. Methodologically, I work with deep learning, NLP, image-as-data approaches, word embeddings, network analysis, and scalable tools for analyzing large digital datasets.

My academic background is in quantitative political science from the University of Copenhagen, complemented by training in social data science, econometrics, machine learning, and computational modeling. More broadly, I am interested in developing and applying computational methods that make it possible to study social life at scale without losing sight of substantive social-scientific questions.