I am a PhD student in Machine Learning at TU Berlin and the Max Planck Institute for Human Cognitive and Brain Sciences. I work on representation learning for deep neural networks in low data regimes, mostly using ideas from probability theory. I am mainly advised by Klaus-Robert Müller and co-supervised by Robert A. Vandermeulen and Martin Hebart, but frequently collaborate with ML researchers in Zurich, Copenhagen, Toronto and Washington. Previously, I was a MSc student in IT & Cognition / Computer Science of Isabelle Augenstein, Johannes Bjerva, and Maria Barrett, mainly supervised by Johannes and Isabelle.
My research mainly revolves around representation learning. In particular, I work on robust representations and out-of-distribution generalization in low data regimes across different fields. In so doing, I use neural networks, approximate Bayesian methods (such as variational inference) and other approaches from probability theory. Mostly, however, I am debugging, examining model behaviour, using print statements, and contemplating why things are not working. Have a look at the projects or publications section for more information about my work and interests. Feel free to reach out to me, if you believe our research intentions are aligned and you are keen to collaborate on a project.