I am a PhD student from Munich, Germany, working on deep learning for 3D medical data in the form of finite-element meshes and point clouds. I employ graph-convolutional neural networks (GCN) and use symmetry (geometric deep learning) and physics-informed deep learning to boost their performance.
Additionally, I am interested in the mathematical foundations of deep learning through the application of functional analysis and identification of the interplay with dynamical systems and partial differential equations.
I am excited about (super)computers, especially Linux-based systems and clusters. Consequently, I wrote my master's thesis on Hessian-based optimisation of deep neural networks in the context of high performance computing (HPC).
Open master thesis project: I am currently looking for a student to work on the interface of neural ODEs, optical flow and physics-informed neural networks (PINN) for blood flow in arteries. If you like programming (Python, JAX), deep learning and PDEs feel free to reach out to me!
Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.