🎉The 8th edition of DataFest Germany will be held at Ludwig-Maximilians-Universität in Munich from 28 March to 30 March 2025. relAI is proud to support the event organization again this year. Additionally, a team of relAI students will participate in this exciting competition and networking opportunity.
The event is an annual data-driven competition, commonly referred to as a “hackathon,” that alternates between Mannheim and Munich. It is organized in collaboration with partners from industry and research institutions.
Datafest Germany is a celebration that follows upon the model DataFest™, organized by the American Statistical Association. The world's first DataFest took place at the University of California in 2011. Since then, many universities took up the DataFest format.
Title:Â Foundational Methods for Foundation Models for Scientific Machine Learning
Date and Time: March 26, 2025 14:00 CET
Location: Lecture Hall W201, Professor-Huber-Platz 2, LMU Munich, 80539 Munich (Metro U3/U6 Universität, Exit B) LMU Room Finder
Abstract
The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable, when built on the rich, diverse, multimodal data that are available from large-scale experimental and simulational data in scientific computing (SC), broadly defined. Such models could provide a robust and principled foundation for scientific machine learning (SciML), going well beyond simply using ML tools developed for internet and social media applications to help solve future scientific problems. Prof. Mahoney will describe recent work demonstrating the potential of the "pre-train and fine-tune" paradigm, widely-used in CV and NLP, for SciML problems, demonstrating a clear path towards building SciML foundation models; as well as recent work highlighting multiple "failure modes" that arise when trying to interface data-driven ML methodologies with domain-driven SC methodologies, demonstrating clear obstacles to traversing that path successfully. Prof. Mahoney will also describe initial work on developing novel methods to address several of these challenges, as well as their implementations at scale, a general solution to which will be needed to build robust and reliable SciML models consisting of millions or billions or trillions of parameters.
Bio of the speaker
Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as head of the Machine Learning and Analytics Group at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, scientific machine learning, scalable stochastic optimization, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, physics informed machine learning, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he was on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council's Committee on the Analysis of Massive Data, he co-organized the Simons Institute's fall 2013 and 2018 programs on the foundations of data science, he ran the Park City Mathematics Institute's 2016 PCMI Summer Session on The Mathematics of Data, he ran the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets, and he was the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/ .
This event is open to everyone; registration is not required.
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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 fĂĽr 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.  Â
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.
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.
relAI warmly welcomes LMU Professor David RĂĽgamer 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. RĂĽgamer 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. RĂĽckert 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.
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.
