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.
It is our great pleasure to announce the next Munich AI Lecture featuring Prof. Dr. Jean-Luc Starck, Director of Research and head of the CosmoStat laboratory at the Institute of Research into the Fundamental Laws of the Universe, Département d'Astrophysique, CEA-Saclay, France. The lecture is organized by relAI director Prof. Dr. Gitta Kutyniok, and co-hosted by Prof. Dr. Jochen Weller,with support of BAIOSPHERE, the Bavarian AI Network.
Event Details:
Speaker: Prof. Dr. Jean-Luc Starck
Title: Unveiling the Cosmos: Deep Learning Solutions to Inverse Problems in Astrophysics
Date and Time: Tuesday, 18. February 2025 from 17:00 pm to 18:30 pm
Prof. Starck will speak about how inverse problems in astrophysics, such as image reconstruction or gravitational lensing data analysis, have traditionally relied on sparsity-based techniques to recover underlying physical structures from incomplete or noisy data. Deep learning methods are now replacing these classical approaches, offering unprecedented performance gains in accuracy and efficiency. Despite their success, deep learning methods introduce new challenges, including interpretability, generalization across diverse astrophysical scenarios, and robustness to observational biases. In this talk, the speaker will explore the transition from sparsity-driven methods to deep learning-based solutions, highlighting both the opportunities and pitfalls of this paradigm shift. Prof. Starck will discuss recent developments, applications to astrophysical data, and future directions for addressing the emerging challenges in this rapidly evolving field.
To read more information about the event and the speaker, visit this weblink.
relAI is a co-organiser of the Munich AI lectures. Find more info on this and other upcoming events on the Munich AI lectures home page.
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This month, a team of 13 talented master and PhD students from our graduate school in reliable AI (relAI) showcased their quantitative skills and teamwork in an exciting estimation competition. The participants had 30 minutes to work on 13 estimation challenges, such as "What is the average discharge of the Isar when it meets the Donau in m^3/s?"
The spirit of competition and learning was truly inspiring. Check out the photo of our team, proudly representing relAI.
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We are pleased to share that on January 29, 2025, relAI will join the opening ceremony of our partner AI-HUB@LMU to celebrate its founding. AI-HUB@LMU is a platform that, for the first time, unites all 18 faculties of Ludwig-Maximilians-Universität München as a joint scientific community and aims to advance research, teaching, and transfer in artificial intelligence and data science at LMU.
As part of our commitment to fostering collaboration and innovation, relAI supports the organization of this significant event. The event will be honoured by inaugural remarks from representatives of the university and government. All 18 faculties will then present their highlights in AI and data science in keynote talks, panel discussions, pitch talks, and presentations. Check this link for the full program: https://www.ai-news.lmu.de/grand-opening-ai-hub.
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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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Multi-Head Attention has become ubiquitous in modern machine learning architectures, but how much efficiency can still be gained? This question was the focus of Dr. Maximilian Baust’s talk, "Beyond Transformers: Why Beating Multi-Head Attention is Hard."
In his presentation, Dr. Baust explored potential solutions for improving efficiency, ranging from implementation strategies and algorithmic modifications to new architectures, including spiking neural networks.
Dr. Maximilian Baust serves as Director of Solution Architecture Industries EMEA at NVIDIA and is also an industry mentor for one of relAI’s PhD students.
We extend our gratitude to Dr. Baust for sharing his insights and to our director, Gitta Kutyniok, for inviting him to relAI.
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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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The recent relAI Collab Accelerator Workshop brought together researchers to share their work, explore new ideas, and identify potential collaborations. Here's a brief overview of the event:
The day began with the participants pitching their research topic from 9:00 to 11:00, followed by a coffee break until 11:15. After the break, participants engaged in one-to-one sessions until 14:00, followed by lunch, discussion, and feedback.
Participants contributed diverse topics in the field of Machine Learning and Artificial Intelligence. Mohamed Amine Ketata discussed Generative AI for Graphs, Max Beier presented on Learning Operator of Dynamical Systems, Richard Schwank explored Robust Aggregation through the Geometric Median, and Yurou Liang delved into Differentiable Learning for Causal Discovery.
The workshop was fertile ground for generating new research ideas and possible collaborations. During the one-to-one discussions, participants identified several projects for cooperation, such as principled modifications of loss functions to enhance robustness against outlier data rows.
Participants gained new insights into their research during the event. For example, one participant discovered a probabilistic approach to their forecasting issue without relying on a model. Another learned about structure learning as it applies to tabular data, which provided a temporal interpretation of the data. One researcher was challenged about the convexity of their problem. Discussions highlighted intriguing applications of median aggregation techniques to abstract spaces, connecting concentration inequalities with uncertainty quantification.
The relAI Collab Accelerator Workshop was an enriching experience, offering a platform for researchers to connect, share insights, and pave the way for future collaborations. The feedback during this first iteration will help refine the format and make it even more engaging. We are looking forward to the next iteration!
relAI thanks Max Beier and Richard Schwank for their initiative and the organization of the event.
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Congratulations!
The recent work of relAI PhD student Lukas Gosch has won the Best Paper Award at the 3rd AdvML-Frontiers workshop at the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). The workshop and paper presentation took place at the Vancouver Convention Center in Canada on December 14th, 2024.
Lukas is a PhD student at relAI, advised by the relAI Co-Director Prof. Dr. Stephan Günnemann. His research focuses on robust and reliable machine learning, as well as machine learning on graphs.
The award-winning paper „Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks“, that Lukas authored together with Mahalakshmi Sabanayagam and relAI Fellows Debarghya Ghoshdastidar and Stephan Günnemann, develops the first architecture-aware certification technique for common neural networks against poisoning and backdoor attacks.
Our sincerest congratulations to Lukas and his co-authors on this achievement!
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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.
