Supporting AI skill in socially disadvantaged children
Fellow
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The Roland Berger Foundation (RBS) and TUM have begun a collaboration to promote the AI skills of socially disadvantaged children and young people. RBS works with 70 partner schools throughout Germany to provide scholarships to talented primary school pupils from the second grade onwards from socially disadvantaged families.
relAI Fellow Enkeledja Kasneci is the scientific director of the project. The scholarship holders learn how to use AI responsibly and reflectively. AI tools are also being developed to better support children and young people with difficult starting conditions
The European legal initiative to regulate AI (Artificial Intelligence Act) poses a particular challenge to small and medium-sized enterprises (SMEs) and start-ups that want to benefit from artificial intelligence and pursue innovations. Bavarian AI Act Accelerator, a new project funded by the Bayerisches Staatsministerium für Digitales and coordinated by the appliedAI Institute for Europe gGmbH, is designed to support companies in fulfilling the new requirements and, therefore, lower barriers to the use of artificial intelligence.
Principal contributors of the project include relAI directors Prof. Dr. Gitta Kutyniok and Prof. Dr. Stephan Günnemann, relAI fellow Prof. Dr. Mark Zöller, as well as scientists from the Technical University of Munich (TUM) and the University of Technology Nuremberg (UTN), who provide the necessarily high degree of interdisciplinarity.
relAI director Prof. Dr. Gitta Kutyniok leads the scientific part of the project. One of the main goals is to develop a system for automatic, and hence easy and fair, verification with the EU AI Act. This requires the following steps: 🔹Derive a profound legal understanding of the different terminologies. 🔹Develop a formalization/mathematization of the articles. 🔹 Build a system for automatic verification.
🛫 Our director, Gitta Kutyniok, gave a talk and joined the panel discussion at the Kick-Off event last week (photo) as the scientific lead of the project.
relAI warmly welcomes LMU Professor David Rügamer to our school. David heads the Data Science Group at LMU, and he is also a Principal Investigator at the Munich Center for Machine Learning (MCML).
Prof. Rügamer works on fundamental topics within the relAI research area Mathematical and Algorithmic Foundations applied to neural networks, such as symmetries, sparsity, and uncertainty quantification in deep neural networks. Additionally, his work is also relevant to the Algorithmic Decision-Making relAI research topic. relAI will benefit from his research experience and, furthermore, from his contributions to our Curriculum, including lectures..
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In this interview, relAI Fellow Daniel Rückert, recently awarded Germany’s highest research distinction, the Gottfried Wilhelm Leibniz Prize, shares his insights on the role of artificial intelligence (AI) systems in medicine.
Prof. Rückert discusses the significant potential of AI in early disease diagnosis, prevention, and personalized treatment, and explains his contributions to AI-assisted analysis of X-ray and MRI images, focusing on the detailed detection of abnormalities and the quick reconstruction of high-quality images. Notably, he emphasizes that reliability and explainability are essential aspects of AI systems in medicine and one of his research topics at relAI.
relAI warmly welcomes TUM Professors Lorenzo Masia and Bene Wiestler to our school. With the addition of these two excellent fellows, relAI will enhance its research areas “Robotics & Interacting Systems” and “Medicine & Healthcare”.
Lorenzo Masia is a professor of “Intelligent BioRobotic Systems” and serves as the Deputy Director of the Munich Institute for Robotics and Machine Intelligence (MIRMI) at TUM. His research focuses on Rehabilitation Robotics and ExoSuits. His work involves developing reliable AI systems for human augmentation and assistance in medical contexts, which aligns perfectly with the mission of relA.
Bene Wiestler is a professor of “AI for Image-Guided Diagnosis and Therapy” at the TUM School of Medicine and Health. His interdisciplinary approach merges medicine with machine learning, focusing on the research and application of advanced artificial intelligence models to tackle important clinical challenges. A key aspect of his work relevant to relAI is the development of safe and reliable AI models for medical applications.
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We are proud to announce that relAI Fellow Prof. Solveig Vieluf won the 2024 Young Investigator Award of the American Epilepsy Society (AES). Her abstract, titled “Seizure Monitoring with Combined Diary and Wearable Data - a Multicenter, Longitudinal, Observational Study”, was selected from over 1,500 submissions for this honor. This award recognizes 20 young investigators conducting basic, translational, or clinical epilepsy research.
She presented her work at the AES Annual Meeting in early December 2024.
Congratulations!
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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
