I am a research group leader at Helmholtz Munich. My goal is to build machine learning models capable of approaching the performance of biological brains in terms of flexibility to changes in tasks and environments. Drawing inspiration from adaptation behavior of biological systems, I study methods for domain adaptation and self-supervised learning and build machine learning tools for robust scientific inference in neuroscience and other life sciences.

I pursued my doctoral studies at the Max Planck International Research School for Intelligent School and the Swiss Federal Institute of Technology Lausanne (EPFL), advised by Matthias Bethge and Mackenzie Mathis in the ELLIS PhD & PostDoc program.

During my PhD, I also worked as a Research Scientist Intern advised by Laurens van der Maaten and Ishan Misra on multimodal representation learning in the FAIR team at Meta NYC, and at Amazon Web Services in Tübingen as an Applied Science Intern where I worked on self-learning and object centric representations with Peter Gehler, Bernhard Schölkopf and Matthias Bethge.

Prior to starting my PhD, I worked on wav2vec and vq-wav2vec, two self-supervised representation learning algorithms for speech processing with Michael Auli, Alexei Baevski and Ronan Collobert at Facebook AI Research in Menlo Park, CA.

Aside from my research, I’m a strong supporter of exposing children to modern computer science topics early on during their school education. That’s why I co-founded and advised IT4Kids to teach CS in elementary school, KI macht Schule to teach AI and Machine Learning fundamentals in high school and helped organizing the German National Competition in AI for high school students.