relAI Fellow Daniel Rückert receives Leibniz Prize
Fellow
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relAI is proud to announce that relAI fellow Prof. Daniel Rückert has been awarded the Gottfried Wilhelm Leibniz Prize 2025. This prestigious award, regarded as the most important German research prize, is endowed with 2.5 million euros by the German Research Foundation (DFG).
The professor of Artificial Intelligence (AI) in Medicine and Healthcare at the Technical University of Munich (TUM) has been honored for his research on AI-assisted medical imaging. He has developed pioneering methods with which AI algorithms can generate particularly informative images from computer tomography or magnetic resonance imaging, analyze them, and interpret them for improved medical diagnostics.
At relAI, he focuses on the safe and privacy aspects of the Medicine & Healthcare research area. His research encompasses several key topics, including reliable machine learning for medical imaging and sensing, privacy-preserving AI, trustworthy medical foundation models, and safe and responsible clinical AI. As a member of the relAI Steering Committee, he represents the Medicine & Healthcare area, playing a crucial role in shaping the organization's goals and future directions of the school.
Enkelejda Kasneci is a Distinguished Professor (“Liesel Beckmann Distinguished Professorship”) for Human-Centered Technologies for Learning at the TUM School of Social Sciences & Technology. Her research focuses on Human-Computer Interaction and developing AI systems that sense and infer the user’s cognitive state, expertise, actions, and intentions based on multimodal data.
Prof. Dr. Enkelejda Kasneci together with her colleague Prof. Dr Tina Seidel, both directors of the TUM EdTech Center, received the Heinz Maier-Leibnitz Medal 2024 in recognition of their “outstanding research in the field of digital teaching and learning as well as their commitment to establishing the TUM Center for Educational Technologies.”
With the Deutscher Zukunftspreis, the German Federal President acknowledges outstanding achievements in technology, engineering, and science, as well as software and algorithm-based innovations that significantly expand the international state of research and technology and are already being used in practice. The prestigious award is endowed with 250,000 euros.
Björn Ommer and his team have developed a groundbreaking Stable Diffusion Model for image generation, which serves as a foundational technology for many AI models, including those from Google and OpenAI.
We’re proud to have Prof. Ommer in our relAI family.
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Congratulations to relAI fellow Johannes Maly on winning the 2024 ACHA Charles Chui Young Researcher Best Paper Award!
Johannes received this recognition for his paper, “Robust Sensing of Low-Rank Matrices with Non-Orthogonal Sparse Decomposition,” published in November 2023 (Vol. 67, ACHA). His research focuses on understanding how to leverage intrinsic models or data structures and how the loss of information caused by digital processing (quantization) affects theoretical results. This is highly relevant for resource-aware and reliable AI
Daniel Rückert holds the Alexander von Humboldt Professorship for AI in Medicine and Healthcare at TUM. His research focuses on developing advanced algorithms for biomedical image analysis, including segmentation, registration, and deep learning methods to support reliable AI in healthcare applications.
The MICCAI Society (Medical Image Computing and Computer-Assisted Intervention) recognized Daniel Rückert for his outstanding, lasting contributions to the field. His work has significantly advanced the application of AI in biomedical imaging, helping create reliable, clinically impactful solutions.
This recognition honors his pioneering work on Stable Diffusion, a generative AI model that has democratized image generation. Stable Diffusion enables users to create high-quality images from text descriptions, making advanced image generation accessible through open-source software without the need for expensive hardware. This innovation exemplifies our mission at relAI, which focuses on advancing reliable AI systems.
The German AI Award, presented by WELT, recognizes innovations that drive the future of AI both in theory and application. Björn Ommer’s recognition reflects his significant role in shaping the evolution of reliable and transparent AI systems.
relAI is proud to announce the addition of five exceptional fellows to the school. Enkelejda Kasneci (TUM), Gjergji Kasneci (TUM), Björn Ommer (LMU), Tom Sterkenburg (LMU), and Abdalla Swikir (TUM) have each made significant contributions to their respective fields and bring a wealth of knowledge and experience to our family.
The research topics of the new fellows tackle essential aspects of the field of reliable AI. Their work ranges from the study of human-computer interaction and semantic scene understanding to the study of fairness and inductive bias in machine learning as well as safe learning in robotics. Their engagement in the school's research and educational activities will contribute to the reliable application of AI in real-life scenarios, such as improving user experience in digital interfaces and enhancing the safety of autonomous systems.
Enkelejda Kasneci is a Distinguished Professor (“Liesel Beckmann Distinguished Professorship”) for Human-Centered Technologies for Learning at the TUM School of Social Sciences & Technology. Her research focuses on Human-Computer Interaction and developing AI systems that sense and infer the user’s cognitive state, expertise, actions, and intentions based on multimodal data.
Gjergji Kasneci holds the Chair for Responsible Data Science at TUM School of Computation, Information & Technology. His research focuses on transparency, robustness, bias, and fairness in machine learning algorithms, incorporating ethical, legal, and societal considerations.
Abdalla Swikir is a Senior Scientist and Teaching Coordinator at the TUM Munich Institute of Robotics and Machine Intelligence (MIRMI). His research in safe learning for robotic control and autonomous systems targets the enhancement of reliability and safety, ensuring these technologies can effectively function in dynamic and critical environments.
Björn Ommer is Head of the LMU Computer Vision & Learning Group. His research interests include semantic scene understanding and retrieval, generative AI and visual synthesis, explainable AI, and self-supervised metric and representation learning. Moreover, he is applying this basic research in interdisciplinary projects within neuroscience and the digital humanities.
Tom Sterkenburg is an Emmy Noether junior research group leader at the Munich Center for Mathematical Philosophy at LMU Munich. His Emmy Noether project, “From Bias to Knowledge: The Epistemology of Machine Learning", is concerned with clarifying the fundamental concept of inductive bias in machine learning.
“For his impressive track-record and contributions in the field of dependable systems, including multiple publications in highly regarded venues, and influence on practical dependable systems.”
The award aims to recognize a junior researcher, “who demonstrates outstanding potential for creative ideas and innovative research in the field of dependable and resilient computer systems and networks”.
Each of them brings their own expertise and insights that will further enrich our research agenda, educational offers, and scientific community. They are dedicated to making significant contributions to the advancement of reliable AI, particularly in Medicine & Healthcare and AI in Education areas.
Nassir Navab is a full professor and director of the Laboratories for Computer Aided Medical Procedures at TUM and adjunct professor at John Hopkins University. One focus of his research is AI assisted Surgery, where reliable methods are a key requirement for both clinicians and patients.
Solveig Vieluf is a professor of AI-based telemonitoring in the field of cardiology at LMU. Previously, she has also worked on epilepsy and aging research. She uses methods from explainability to explore influence factors on model performance.
The research of Tobias Lasser is focused on computational imaging and inverse problems in medicine and healthcare. In his work on clinical decision support using AI, he works on prioritization of critical cases for treatment.
Jochen Kuhn works on the intersection of AI and education, in particular on the use of these future technologies to foster learning and teaching in STEM disciplines. He is a professor of Physics education at LMU. Reliability is important in his research, particularly the role of bias and inaccurate information from AI chatbots on learning and teaching.
Join us in welcoming these four to the relAI community!
Julia Schnabel is Professor for Computational Imaging and AI in Medicine at the Technical University of Munich TUM (Liesel Beckmann Distinguished Professorship), and Director at the Institute for Machine Learning in Biomedical Imaging at Helmholtz Munich (Helmholtz Distinguished Professorship). Since 2015, she has also been Professor of Computational Imaging at King's College London.
Prof. Schnabel works in the field of medical image processing and machine learning. Her research focuses on the areas of intelligent imaging up to clinical evaluation, including complex motion modeling, image reconstruction, quality assurance, segmentation, and classification applied to multimodal, quantitative, and dynamic imaging.
