Focus area: Trans-disciplinary Applications

I am currently a PhD student in the group of Fred Hamprecht at the Interdisciplinary Centre for Scientific Computing (IWR) of Heidelberg University. My background is physics and I am primarily interested in how we may use our intuition and tools from theoretical physics to understand and improve machine learning methods. I first became interested in this during my masters thesis where (among other things) I worked on a grand canonical MCMC sampling algorithm for inference problems. [1] In my PhD, I am working on inference problems on manifolds particularly the simplex (ie inference problems where the parameters are distributions themselves). The physical methods we use mostly stem from differential geometry (as in general relativity and information geometry) and our applications are also in physics, namely cosmology.

I am a PhD student of the ELIZA Zuse School in Berlin, where I am working with Frank Noé and Klaus-Robert Müller on AI4Science. My research focuses on generative models for dynamical biophysical systems. I also just love geeking out about all things computer science — from machine learning and physics to weird algorithms and cool hacks.

Matthias Hein is Bosch endowed Professor of Machine Learning and  the coordinator of the international master program in machine learning at the University of Tübingen. He is member of the Excellence Cluster “Machine Learning: New Perspectives for Science” and the Tübingen AI Center. His main research interests are to make machine learning systems robust, safe and explainable and to provide theoretical foundations for machine learning, in particular deep learning. He serves regularly as area chair for ICML, NeurIPS or AISTATS and has been action editor for Journal of Machine Learning Research (JMLR) from 2013 to 2018. He is an ELLIS Fellow and has been awarded the German Pattern recognition award, an ERC Starting grant and several best paper awards (NeurIPS, COLT, ALT).

Wojciech Samek is a professor at TU Berlin, head of the AI Department at Fraunhofer HHI, and a fellow at BIFOLD and ELLIS Unit Berlin. His research focuses on explainable AI (XAI), covering method development, theoretical studies, and applications in medicine and geoscience. His pioneering contributions to XAI include key methods like Layer-wise Relevance Propagation (LRP) [1] and advancements in concept-level explainability, evaluation of explanations, and XAI-based model- and data improvement.

He has edited two books on XAI, served as a senior editor for IEEE TNNLS and associate editor for various other journals, and held area chair roles at NeurIPS, ICML, and NAACL. He is a member of Germany’s Platform for AI and serves on the boards of AGH University’s AI Center, HEIBRiDS, and ELIZA. He has received multiple best paper awards, including from Pattern Recognition (2020), Digital Signal Processing (2022), and the IEEE Signal Processing Society (2025).

[1] Bach, S, Binder, A, Montavon, G, Klauschen, F, Müller, K-R, Samek, W (2015). On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation. PLOS ONE, 10(7):e0130140.

Prof. Wachinger conducts research on novel AI algorithms for the analysis of medical images and their translation into clinical practice. He develops multimodal models for disease prediction and uses big data to train complex neural networks. Currently, he is focusing on the following challenges: (i) transparency of AI, (ii) integration of heterogeneous data, and (iii) generalization, bias, and fairness.

Prof. Wachinger studied computer science at TUM and ENST Paris. He holds an Honours Degree in Technology Management from CDTM. In 2011, he received his PhD in medical image analysis from TUM. As a post-doc, he was at the Massachusetts Institute of Technology in Cambridge and Harvard Medical School in Boston, USA. Subsequently, he took over an interims-professorship at the Ludwig-Maximilians-University of Munich. In 2021, he was appointed to the professorship for AI in radiology at TUM.

Prof Schnabel’s (*1969) field of research comprises medical image computing and machine learning. Her research focuses on intelligent imaging solutions and computer aided evaluation, including complex motion modelling, image reconstruction, image quality control, image segmentation and classification, applied to multi-modal, quantitative and dynamic imaging.

Since 2021 Julia Schnabel is Professor for Computational Imaging and AI in Medicine at TUM (TUM Liesel Beckmann Distinguished Professorship), jointly with Helmholtz Center Munich (Helmholtz Distinguished Professorship). She studied at TU Berlin (1993) and did a PhD at University College London (1998), followed by Postdocs at UMC Utrecht, King’s College London, and UCL. In 2007 she became first Associate Professor and in 2014 Full Professor of Engineering Science at University of Oxford, and from 2015 Chair in Computational Imaging at King’s College.