I am a Ph.D. student at the University of Waterloo working with Professor Shai Ben-David. Before moving to Waterloo, I did my masters at the University of Tübingen in Cognitive Science. I wrote my master thesis on "Domain Adaptation and Causal Assumptions" at the Max-Planck Institute for Intelligent Systems under the supervision of Professor Ruth Urner. Prior to that, I did my bachelor's degree in Mathematics at Ludwig-Maximilians-Universität, Munich.
I am interested in facilitating a better understanding of Machine Learning models and their limitations by means of a mathematical analysis. I aim to find intuitive assumptions that are meaningful in practice and lead to formal guarantees in scenarios in which common statistical assumptions break down, such as transfer learning. I believe that by developing tools that make the limitations of a model more explicit, it becomes easier to assess the trustworthiness of a model's prediction. My current research also focuses on identifying and characterizing potential sources of unfairness in automated decision making and developing new interpretable frameworks in which to address the issue of algorithmic fairness.