Vincent Bürgin

Hi, I’m Vincent! I’m a computer science generalist interested in a wide range of things.
I recently graduated from the M.Sc. Informatics program at the Technical University of Munich (TUM). In the last years, I had the chance to be involved in a number of research projects in the machine learning and medical imaging fields (in the TUM DI-LAB at the CAMP chair, at the Theoretical Foundations of AI chair, at ImFusion, and at the Lab for AI in Medicine), and several projects led to published papers.
I also have a passion for mathematics, and completed a B.Sc. in Mathematics alongside my B.Sc. in Computer Science at the University of Passau. I like to work somewhere close to the border between computer science and mathematics, such as with my Master’s thesis, which explored a topological loss functions rooted in persistent homology, and my Bachelor’s thesis, which was about the properties of a particular stochastic order and whether or not it can be used for game theory.
I’ve worked with a variety of programming languages and technologies, and I’m always keen to learn new ones. Python is my go-to language for machine learning and most day-to-day coding needs. More recently I’ve began to use more C++, and of course I love Rust and try to find a reason to use it whenever I can. I’m also using JavaScript for interactive visualizations and creative coding, and have used C# for a lot of different projects before I started my studies. I’ve also used Haskell, Julia and several other languages in projects.
news
Oct 15, 2024 | I published my post on the relAI blog: "Using topological features to prevent topological errors" |
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Sep 24, 2024 | This site goes online! |
publications
- Topologically faithful multi-class segmentation in medical imagesMedical Image Computing and Computer Assisted Intervention (MICCAI), 2024
- Efficient Betti Matching Enables Topology-Aware 3D Segmentation via Persistent HomologyarXiv preprint: arXiv:2407.04683, 2024
- Robust vertebra identification using simultaneous node and edge predicting Graph Neural NetworksIn International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023
- S3M: scalable statistical shape modeling through unsupervised correspondencesIn International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023
- On the Localization of Ultrasound Image Slices Within Point Distribution ModelsIn International Workshop on Shape in Medical Imaging, 2023
- Remarks on the tail order on moment sequencesJournal of Mathematical Analysis and Applications, 2022