Focus area: Trans-disciplinary Applications

Jun.-Prof. Dr. Maria Kalweit holds the Tenure-Track CRIION Professorship for Bioinformatics: AI for Oncology Research at the Department of Computer Science, University of Freiburg. She earned her PhD in Computer Science from the University of Freiburg in 2022 under Prof. Joschka Boedecker. She also serves as Chief Scientific Officer at the Collaborative Research Institute Intelligent Oncology (CRIION) of the Mertelsmann Foundation gGmbH. Her research centers on robust, efficient, and explainable machine learning for oncology, with a focus on limited-data settings, biological variability, and technical heterogeneity. Her work has resulted in multiple patents and a digital biomarker. Her paper “Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller” received the Best Paper Award at IJCNN 2018. She has further been recognized with the Wolfgang-Gentner-Nachwuchsförderpreis (2023) and the Gips-Schüle-Nachwuchspreis in Technikwissenschaften (2024).

Jakob has been Professor for “Machine Learning in Science” since May 2020. The W3 professorship has been set up as part of the Cluster of Excellence “Machine Learning: New Perspectives for the Sciences”. He is also an Adjunct Research Scientist at the Max Planck Institute for Intelligent Systems, Director of the Bernstein Center for Computational Neuroscience, and an ELLIS Fellow and member of the ELLIS Unit Tübingen. He serves as a speaker of the DFG Collaborative Research Center SFB 1233 Robust Vision, the Excellence Cluster Machine Learning: New Perspectives for Science and the EKFS-training group ClinBrAIn: AI for Clinical Brain Research.

Jakob studied mathematics at Oxford University, worked as a PhD student at the Max Planck Institute for Biological Cybernetics in Tübingen, as a postdoc at the Gatsby Unit at University College London, and as a Bernstein Fellow in Tübingen. He was a Max Planck Group Leader at the Caesar Research Centre in Bonn, a Professor at the Centre for Cognitive Science at TU Darmstadt, and from 2018 to 2020, Professor of Computational Neuroengineering at TU Munich. He was a member of the Young Academy at the German Academy of Sciences Leopoldina (2013-2018), and a FENS Kavli Scholar of Excellence (2018-2023).

Jean-Philippe Vert is the co-founder and CEO of Bioptimus, an AI-first tech company pioneering the use of foundation models to transform our understanding of biology and accelerate biomedical innovation. He is also a professor (currently on leave) at PSL University. A recognized leader in AI for biology, Jean-Philippe brings over 25 years of experience at the cutting edge of machine learning and life sciences. Before founding Bioptimus, he served as Chief R&D Officer at Owkin and was a Research Lead at Google Brain. Prior to transitioning to industry, he held academic positions at ENS Paris, the Curie Institute, and Mines ParisTech, and was a Fullbright and Miller visiting professor at the University of California, Berkeley. He began his research career at Kyoto University. Jean-Philippe graduated from École Polytechnique and the Corps des Mines, and earned a PhD in mathematics from Paris University. He has authored over 190 scientific publications and is internationally recognized for his contributions to statistical learning, artificial intelligence, biomedical data modeling, and translational research. He is an ELLIS Fellow, a member of the National Academy of Technologies of France, and has received several prestigious honors, including the CNRS Bronze Medal, a Grand Prize from the National Academy of Sciences of France, and a European Research Council (ERC) grant.

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.