About Me

I am a Ph.D. student in the Department of Computer Science at Aarhus University and a member of the Algorithms, Data and Artificial Intelligence research section. My work focuses on the theoretical foundations of machine learning, and I am supervised by Professor Kasper Green Larsen. During my Ph.D., I aim to understand the fundamental limits of learning from data and which attributes determine the success and failure of learning algorithms.

Before starting research in computer science, I completed a master's degree in mathematics at Aarhus University. During my studies, I was especially inspired by abstract algebra and category theory, which was the topic of my master's thesis.

Outside of academia, I enjoy playing video games and climbing. If you're looking for me in a seminar room, I'll probably be the one crocheting.

Research Interests

I am interested in the theory of machine learning, with an emphasis on understanding the mathematical conditions that enable or preclude learning from data. A recurring theme throughout my work, and more broadly in how I approach problems, is to study how small changes in formulations or assumptions impact the learnability of a problem and the guarantees one can obtain. I am particularly interested in new variations of learning problems, and in how minor changes can lead to qualitatively different results. I am always happy to discuss such variations and related questions.

Keywords: Learning theory, supervised learning, generalization bounds, margin-theory

Publications

Teaching