relAI contribution to Bavarian AI Act Accelerator: helping companies implement the EU´s AI Act

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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.   

Excerpts taken from:

After Stargate and DeepSeek, which technological developments will influence the future of the AI race? What implications does this hold for Germany and Europe? Have we constrained ourselves too soon with the AI Act? In an interview on the “Plattform Lernende Systeme” website, Prof. Dr. Gitta Kutyniok, Director of relAI and member of the Platform, discusses the current dynamics and explains how mathematics can enhance the comprehensibility of AI results.

Watch the interview here.

The Saxon-Bavarian AI project GAIn – Next Generation AI Computing is a pilot project tackling new AI hardware and software concepts to reduce energy consumption and increase reliability for different applications such as surgical robotics. It builds on the foundation of the Cluster of Excellence CeTI, the 6G-life research hub, and the Konrad Zuse Schools of Excellence SECAI and relAI. The project aims to address key challenges in energy consumption, predictability, reliability, and legal implementation. A core objective is to significantly reduce the energy consumption of AI-based applications while enhancing their predictability and reliability for different applications such as surgical robotics.

The project has now been officially launched. Together with Frank Fitzek (TU Dresden), Gitta Kutyniok (LMU, relAI) and Holger Boche (TUM), Stefanie Speidel (TU Dresden, SECAI) hosted the kick-off meeting of the project.  The cooperation across federal states will strengthen Germany's technological sovereignty and contribute to the international leadership role of Saxony and Bavaria in central computing technologies.

Excerpts from the TUD press release AI project "GAIn" with TUD participation aims to propel Saxony and Bavaria to an international leadership role in computing technologies, of the National Center of Tumor Disease Dresden (NCT)  GAIn (2024 – 2027)  and of SECAI news https://secai.org/content/news/56

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.. 

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.

Follow this link to read the complete interview.

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.

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. 

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!

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.

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.

Explore this outstanding paper here.

Our sincerest congratulations to Lukas and his co-authors on this achievement!