My goal is to build machine learning models capable of approaching the performance of biological brains in terms of data-efficiency and robustness to perturbations and changes in their environment. Drawing inspiration from adaptation behaviour of biological systems, I study methods for domain adaptation and continual, multi-task and self-supervised learning.
I am member of the Max Planck International Research School for Intelligent School advised by Matthias Bethge in Tübingen and the ELLIS PhD & PostDoc program advised by Mackenzie Mathis at the Rowland Institute at Harvard and EPFL (from summer 2020).
I spend the past year working on self-supervised representation learning for speech processing with Michael Auli, Alexei Baevski and Ronan Collobert at Facebook AI Research in Menlo Park, CA. For my Master's thesis, I worked on Domain Adaptation in Brains and Machines with Matthias Bethge and Alexander Ecker at the Max Planck Institute for Intelligent Systems and University of Tübingen and Jakob Macke at TU Munich.
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.
PhD Candidate in Machine Learning and Neuroscience, from 2019
Intl. Max Planck Graduate School for Intelligent Systems, Tübingen
AI Resident, Self-Supervised Learning for Speech Recognition, 2018 - 2019
Facebook AI Research, Menlo Park, CA
MSc in Neuroengineering, 2016 - 2018
Technical University of Munich
BSc in Electrical Engineering, Information Technology and Computer Engineering, 2013 - 2016
RWTH Aachen University