We are excited to announce the next Munich AI Lecture featuring Prof. Virginia Dignum, a member of the relAI Scientific Advisory Board. She is Professor of Responsible Artificial Intelligence at Umeå University, Sweden, where she leads the AI Policy Lab. She is also senior advisor on AI policy to the Wallenberg Foundations and chair of the ACM’s Technology Policy Council.

 Event Details:

🔹Title: Responsible AI: Governance, Ethics, and Sustainable Innovation

🔹Date and Time: July 9, 2025 6:30 pm

🔹Location: Plenarsaal of the Bavarian Academy of Sciences and Humanities (BAdW), Alfons-Goppel-Straße 11, 80539 Munich

Abstract

As AI systems become increasingly autonomous and embedded in socio-technical environments, balancing innovation with social responsibility grows increasingly urgent. Multi-agent systems and autonomous agents offer valuable insights into decision-making, coordination, and adaptability, yet their deployment raises critical ethical and governance challenges. How can we ensure that AI aligns with human values, operates transparently, and remains accountable within complex social and economic ecosystems? This talk explores the intersection of AI ethics, governance, and agent-based perspectives, drawing on my work in AI policy and governance, as well as prior research on agents, agent organizations, formal models, and decision-making frameworks. Recent advancements are reshaping AI not just as a technology but as a socio-technical process that functions in dynamic, multi-stakeholder environments. As such, addressing accountability, normative reasoning, and value alignment requires a multidisciplinary approach. A central focus of this talk is the role of governance structures, regulatory mechanisms, and institutional oversight in ensuring AI remains both trustworthy and adaptable. Drawing on recent AI policy research, I will examine strategies for embedding ethical constraints in AI design, the role of explainability in agent decision-making, and how multi-agent coordination informs regulatory compliance. Rather than viewing regulation as a barrier, will show that responsible governance is an enabler of sustainable innovation, driving public trust, business differentiation, and long-term technological progress. By integrating insights from agent-based modeling, AI policy frameworks, and governance strategies, this talk underscores the importance of designing AI systems that are both socially responsible and technically robust. Ultimately, ensuring AI serves the common good requires a multidisciplinary approach—one that combines formal models, ethical considerations, and adaptive policy mechanisms to create AI systems that are accountable, fair, and aligned with human values.

More information is available on the website of the Munich AI Lecture. This is the flagship speaker series about AI in Munich, co-organized by relAI

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We proudly invite you to our next Munich AI Lecture. This is the flagship speaker series about AI in Munich, co-organized by relAI.

 Event Details:

• Speaker: Prof. Michael Mahoney (UC Berkeley)
• Title: 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.

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
• Location: Senatssaal, LMU Munich, Geschwister-Scholl-Platz 1, Munich 

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.

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.

We are happy to invite you to the upcoming Munich AI Lecture featuring two distinguished researchers Prof. Holger Hoos from RWTH Aachen University and Prof. Franca Hoffmann from California Institute of Technology. The lecture is organized by the Chair of Mathematics of Information Processing with support by MCML.

In the first talk, “Dynamics of Strategic Agents and Algorithms as PDEs“, Prof. Hoffmann will speak about dynamics of interactions between algorithms and a population.

In the second talk, “Learning, reasoning and optimisation: Adversarial robustness of neural networks”, Prof. Hoos will discuss robustness of neural networks and its verification.

Event Details:

• Speakers: Prof. Dr. Holger Hoos and Prof. Dr. Franca Hoffmann
• Date and Time: December 17, 2024, 16:00 CET (16:00-17:00 talk by Prof. Hoffmann and 17:30-18:30 talk by Prof. Hoos – We will have a small break with coffee/tea and snacks in between)
• Location: Senatssaal, LMU Munich, Geschwister-Scholl-Platz 1, Munich 

Bio Franca Hoffmann

Franca Hoffmann obtained her master’s in mathematics from Imperial College London (UK) and holds a PhD from the Cambridge Centre for Analysis at University of Cambridge (UK). She held the position of von Kármán instructor at Caltech from 2017 to 2020, then joined University of Bonn (Germany) as Bonn Junior Professor and Quantum Leap Africa in Kigali, Rwanda (African Institute for Mathematical Sciences) as AIMS-Carnegie ResearchChair in Data Science, before arriving at the California Institute of Technology as Assistant Professor in 2022.

Bio Holger Hoos

Holger H. Hoos holds an Alexander von Humboldt professorship in AI at RWTH Aachen University (Germany), where he also leads the AI Center, as well as a professorship in machine learning at Universiteit Leiden (the Netherlands) and an adjunct professorship in computer science at the University of British Columbia (Canada). He is a Fellow of the Association of Computing Machinery (ACM), the Association for the Advancement of Artificial Intelligence (AAAI) and the European AI Association (EurAI), past president of the Canadian Association for Artificial Intelligence, former editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and chair of the board of CLAIRE, an organization that seeks to strengthen European excellence in AI research and innovation (claire-ai.org).

relAI is a co-organiser of the Munich AI lectures. Find more info on these other upcoming events on the Munich AI lectures home page.

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We are pleased to invite you to the upcoming Munich AI Lecture featuring renowned researcher Prof. Dr. Helmut Bölcskei hosted by our relAI director Prof. Dr. Gitta Kutyniok, Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU.

Deep neural networks have led to breakthrough results in numerous practical machine learning tasks. In the lecture “The Mathematical Universe behind Deep Neural Networks,” Prof. Dr. Bölcskei will take us on an exciting journey through the mathematical universe behind these practical successes, elucidating the theoretical underpinnings of deep neural networks in functional analysis, harmonic analysis, complex analysis, approximation theory, dynamical systems, Kolmogorov complexity, optimal transport, fractal geometry, mathematical logic, and automata theory.

Event Details:

 • Speaker: Prof. Dr. Helmut Bölcskei

• Date and Time: November 25th, 2024, 5.15 pm

• Location: Große Aula der LMU (Geschwister-Scholl-Platz 1, Room 120, 80539 München)

Helmut Bölcskei is a Professor of Mathematical Information Science at ETH Zurich and has been a Principal Investigator at the Lagrange Mathematics and Computing Research Center in Paris since 2021. After earning his degrees from Vienna University, he completed a postdoctoral fellowship at Stanford University and has held visiting researcher positions at many leading institutions. In addition to his academic achievements, he is also a successful entrepreneur. Professor Bölcskei has received numerous awards and prestigious fellowships and continues to serve as Editor-in Chief for some of the field's most distinguished journals.

Learn more about it here.

Mark your calendars for these interesting talks coming up in the next weeks!

The next talk, by Daniela Rus, is on July 15th, at 5pm at Lichtenbergstraße 2a, 85748 Garching, Auditorium (Ground floor) of Institute for Advanced Study (IAS), TUM and via zoom. She is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.

On Wednesday July 17th, Alexei A. Efros of University of California, Berkeley will discuss whether “We are (still?) not giving data enough credit”. The lecture takes place in cooperation with the ELLIS-Workshop on "Open Problems in Computer Vision & Generative Modelling" on the same day.

Sebastian Scherer, an Associate Research Professor at the Robotics Institute (RI) at Carnegie Mellon University (CMU) will present his approaches, progress, and results on multi-modal sensing on Monday, July 22nd, at TUM Garching Campus, FMI Building, Boltzmannstr. 3, Hörsaal 2 (00.04.011).

relAI is a co-organiser of the Munich AI lectures. Find more info on these other upcoming events on the Munich AI lectures home page.

We are happy to announce that on October 29th, the three DAAD Zuse Schools of Excellence in Artificial Intelligence, ELIZA (Darmstadt), SECAI (Dresden) und relAI (Munich), will celebrate the second joint Meeting in Munich.

The event, co-organized by DAAD and relAI, will be honoured with the presence of representatives from the Bavarian State Ministry for Science and the Arts (StMWK), Federal Ministry of Education and Research (BMBF), TUM and LMU presidents, and DAAD, who will introduce the meeting with opening speeches. The program will be followed by talks from research fellows, students, and industry partners of the Zuse Schools and rounded by a podium discussion.

Preliminary Agenda

The next Munich AI Lecture will take place on Tuesday, June 25th at 5pm at Arcisstr. 21, Room 0790 and via zoom.

Ivan Laptev, visiting professor at MBZUAI and a senior researcher on leave from Inria Paris, will talk about "From Video Understanding to Embodied Agents". Mark your calendars and join us there!

For more information on the Munich AI Lectures, co-organized by relAI, visit https://munichlectures.ai/upcoming/.