Short CV and Research Interests

In July 2023, I joined the chair for Responsible Data Science as PhD student with the goal of advancing the field of artificial general intelligence.

During my Bachelor's and Master's degree in Computer Science at TUM, I gained broad knowledge in machine learning and artificial neural networks. However, I also started to look at the biological side of intelligence by peaking into neurobiology, computational neuroscience as well as philosophical aspects of human cognition. Since then, I am directing my efforts to the gap between natural and artificial intelligence. I gained some hands-on experience in translating biological concepts to computer science and how to make them useful in real-world applications. For my current research focus, I am pursuing a more holistic view on intelligent systems.

In the future, I hope to transfer respective findings into responsible computational approaches.

Thesis and Project Offers

For a list of modules I am involved see here.

I am always open to discuss potential B.Sc./M.Sc. thesis topics or project proposals (e.g. IDP, guided research). In the best case, your interests overlap with AGI or neuro-symbolic appraoches.

Publications

The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting by Florian Kofler, Felix Meissen, Felix Steinbauer, Robert Graf, Eva Oswald, Ezequiel de da Rosa, Hongwei Bran Li, Ujjwal Baid, Florian Hoelzl, Oezguen Turgut et al. Arxiv Preprint, 2023.

Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling by Ivan Ezhov; Kevin Scibilia; Katharina Franitza; Felix Steinbauer; Suprosanna Shit; Lucas Zimmer; Jana Lipkova; Florian Kofler; Johannes C. Paetzold; Luca Canalini et al. Medical Image Analysis, 2023.

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling by Ezhov, Ivan; Rosier, Marcel; Zimmer, Lucas; Kofler, Florian; Shit, Suprosanna; Paetzold, Johannes C.; Scibilia, Kevin; Steinbauer, Felix; Maechler, Leon; Franitza, Katharina et al. Proceedings of the 2nd Machine Learning for Health symposium, 2022.