Munich AI Lecture with Prof. Dr. Sebastian Pokutta
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We are pleased to announce the first Munich AI Lecture of 2026, presented by Prof. Dr. Sebastian Pokutta on the growing role of Artificial Intelligence in scientific discovery. The lectureis organized by the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU, led by relAI Director Gitta Kutyniok.
Details:
Title: How Machines Explore, Conjecture, and Discover Mathematics
📍 Geschwister-Scholl-Platz 01, Raum D209, 80539 München, Deutschland
The lecture focuses on the role of AI as a partner in mathematical research. Prof. Pokuttawill present approaches from the AI4Math initiative that combine optimization, machine learning, and mathematical structure to open up highly complex search spaces. Using the Hadwiger–Nelson problem as an example, he will explain how neural networks can be used to convert mixed discrete-continuous problems into differentiable optimization problems and explore new solution spaces.
About the Speaker
Prof. Dr. Sebastian Pokutta is Vice President of the Zuse Institute Berlin (ZIB) and a Professor of Mathematics at the TU Berlin, with a research focus on Artificial Intelligence and Optimization. He leads, among other initiatives, the Excellence Cluster MATH+ and the Research Campus MODAL and has previously worked in academia and industry, including at MIT, IBM ILOG, and Georgia Tech. His work has been recognized with numerous awards, such as the Gödel Prize, the STOC Test of Time Award, and the NSF CAREER Award.
If you are interested and meet the qualifications, we encourage you to apply by 16. Februar 2026.
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Join us for a lively meetup featuring leading researchers from MDSI, relAI, and experts from relAI Industry Partner SAP, where we will connect, discuss, and explore new ideas.
🗓️ January 20, 2026, 4:00 - 5:30 pm (doors open at 3:30 pm, open networking after the talks)
📍 SAP Labs Munich Campus (MUE03), Friedrich-Ludwig-Bauer-Straße 5, 85748 Garching bei München, Auditorium (AE.76)
Max Beier (relAI): Learning, Optimization, and Uncertainty in Dynamical Systems with Linear Operators Dynamical systems are ubiquitous in data science and robotics, motivating a broad range of methods for learning models from data, optimizing their behavior, and quantifying uncertainty in their predictions. This talk will showcase how linear-operator methods provide a unified way to study learning, optimization, and uncertainty in dynamical systems. Operator learning treats both inputs and outputs as functions and learns mappings between function spaces, which reframes dynamical modeling as the identification of operators from data. Restricting attention to linear operators enables compact representations, output-independent representations, and efficient computation through the use of linear algebra. We will showcase how this framework can be successfully employed to: learn a representation of a dynamical system for optimal control, model distributions over trajectories efficiently, and generate coherent sequences.
Sebastian Gallenmüller (MDSI): SLICES-DE: Digital Research Infrastructure for Computing and Communication SLICES-DE (Scientific Large-scale Infrastructure for Computing/Communication Experimental Studies - Germany) is a digital research infrastructure and Germany’s national node of the European SLICES-RI initiative. It provides a remote-accessible experimental platform for researchers in information and communication technologies (ICT) with structured workflows to support reproducible experiments and long-term data archiving. The infrastructure is designed to be flexible and scalable to a wide range of research domains, including 6G networks, AI, cybersecurity, and cloud-to-edge systems. SLICES-DE aims to provide a research infrastructure for ICT research in Germany, with engagements possible for academic and industry partners.
Parastoo Pashmchi (SAP): Handle your Missing Values Easily: An ML-powered Solution for Filling Data Gaps Every machine learning and artificial intelligence model relies heavily on data, and managing this data, especially when it comes to dealing with missing values, presents a significant challenge for data scientists. Missing data can lead to a loss of vital information needed for training models and can disrupt the patterns or relationships that prediction algorithms rely on. For example, incomplete financial datasets can yield flawed risk assessments, while missing information in clinical trials or engineering sensor data can compromise the validity of study outcomes and fault detection, respectively. Similarly, missing weather data can lead to inaccurate green energy generation predictions, affecting grid management and planning. Our proposed method provides a fast and easy-to-implement algorithm for imputing missing values, emphasising the preservation of the original data distribution and pattern based on the available data. This algorithm overcomes some of the limitations of conventional techniques and provides a theoretical guarantee for effectively recovering the distribution of the missing values. Key features of this algorithm include the quantification of uncertainty for each imputed value and the capability for multiple imputations to consider various potential scenarios. These features enhance the reliability and stability of our machine learning model, offering more reliable predictions. In this session, we will highlight the algorithm's main features and demonstrate its application in predictive models.
Mario Picciani (MDSI): ProteomicsDB: A Multi-Omics and Multi-Organism Resource for Life Science Research ProteomicsDB is an evolving, publicly accessible multi-omics database originally developed to enable the interactive exploration of large quantitative mass spectrometry–based proteomics datasets, which led to developing the first draft of the human proteome. Initially, the HANA-powered platform supported the real-time comparison of protein abundance across human tissues, cell lines, and body fluids, but was subsequently expanded to integrate other omics layers such as transcriptomics, drug-target interactions, protein–protein interactions, and cell viability data, transforming it into a versatile resource for multi-omics life science research. Over further successive updates, ProteomicsDB was expanded to support additional organisms covering nearly all branches of the tree of life facilitating research towards the concept of “One Health” - integrating and broadening our understanding of the interactions between humans, animals, plants, microorganisms, soil, and the environment to ensure a sustainable and resilient future of healthy living. The latest update strengthened its role to facilitate the comprehensive analyses of drug mechanisms, cell sensitivity profiles, and biomolecular signatures to support precision medicine applications. ProteomicsDB’s rich data content makes it an ideal resource for basic to translational research, with applications in systems biology, and drug discovery, and its versatile and extendible architecture makes it an ideal platform for large-scale research initiatives, illustrated by its central role in projects such as ‘The Proteomes That Feed The World’ and ‘Reducing Non-Human Primates in Non-Clinical Safety Assessment’.
The relAI family has grown to include new Fellows, Partners, and Students.
The program has achieved the milestone of 180 published articles, with this number continuing to rise.
A new research area, "Learning & Instruction," has been introduced.
Many relAI students have gained practical experience through research visits, industry internships, project groups, and guided research.
Over 20 events were held, including an international summer school and community activities such as Welcome Days, a retreat, and student-organized seminars and workshops.
This report highlights the impressive development of relAI over the past year, providing an overview of the program along with recent news and insights from relAI Students and Partners.
Enjoy reading!
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🎉We are thrilled to share that relAI Fellow Tom Sterkenburg has been awarded the 2025 Karl-Heinz Hoffmann Prize by the Bavarian Academy of Sciences and Humanities (BAdW). The Award was presented by the president, Markus Schwaiger, at the Academy’s Ceremonial Annual Meeting on 6 December.
The BAdW is a non-university research institution and a community of scholars dedicated to conducting innovative, long-term research that primarily aims to preserve cultural heritage in the humanities. It provides a unique platform in Bavaria for intergenerational networking among top researchers. A key aspect of promoting the younger generation is the annual science prizes.
The Karl-Heinz Hoffmann Prize, donated by the Ulrich L. Rohde family, is awarded alternately in the fields of humanities and natural sciences. Tom Sterkenburg works at the intersection of philosophy, statistics, and computer science. His work combines mathematical modelling, algorithmic simulation, and philosophical analysis to provide new insights into the classic problem of induction, particularly in the context of machine learning. In this way, he is making a groundbreaking contribution to the dialogue between philosophy and data-driven science.
We are excited to announce that the call for applications to the PhD program 2026 of our Konrad Zuse School of Excellence in Reliable AI (relAI) is now open!
The novel, innovative PhD relAI program offers a cross-sectional training for successful education in AI including scientific knowledge, professional development courses and industrial exposure, providing a coherent, yet flexible and personalised training.
Funded applicants will receive a full salary for three years, including social benefits (TV-L E13 of the German public sector). They may receive additional support through travel grants for conference attendance, research stays, or home travel. Doctoral students are hosted by a relAI Fellow who helps them to define their research project. Depending on the affiliation of this hosting fellow, they enrol at TUM or LMU.
We highly encourage you to apply if you have:
an excellent master’s degree (or equivalent) in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines;
a genuine interest to work on a topic of reliable AI covering aspects such as safety, security, privacy and responsibility in one relAI’s research areas Mathematical & Algorithmic foundations, Algorithmic Decision-Making, Medicine & Healthcare, Robotics & Interacting Systems, or Learning and Education;
Please help us in spreading the word, especially to excellent international candidates.
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We are happy to announce that Resaro has joined relAI as an Industry Partner!
Resaro stands for REsponsible - SAfe - RObust. Its mission is to ensure the performance, safety, and security of mission-critical AI systems, which is fundamentally aligned with the four relAI central themes: responsibility, privacy, safety and security.
Resaro’s Approved Intelligence Platform (AIP) provides modular, scenario-based testing workflows to evaluate mission-critical AI systems in defence, public safety, and critical civil use cases. It delivers a comprehensive, end-to-end testing environment based on a proprietary AI trust ontology with measurable AI Solutions Quality Indicators (ASQI) to test, evaluate, verify and validate on solution or system level with different AI modalities. This evaluation covers various aspects, including quality, performance, safety and security. Recent systems under examination have included anti-money laundering solutions, X-ray imaging anomaly classifiers, deepfake detectors, UAVs, face-in-crowd recognition systems, hypothesis generators for pharmaceutical research, customer service chatbots, and video action recognition solutions, among others.
Additionally, Resaro has developed an innovative approach not only to test for quality but also to describe it in a use-case-specific yet standardized manner. For more information, visit www.resaro.ai/asqi.
Partnership with relAI:
🤝Through this mutually beneficial partnership, relAI Students will gain access to internship and research opportunities, while relAI will expand its network by adding unique skills. Additionally, Resaro will strengthen and broaden its open-source trust community, exchanging knowledge with both academic institutions and industry partners.
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It was a remarkable symposium! Yesterday, relAI members had the privilege of hosting numerous guests at the Haus der Bayerischen Wirtschaft to reflect on the topic of responsible transformation through generative AI in science, industry, and society.
The event began with warm welcome addresses from the relAI Directors, State Secretary Dr. Rolf-Dieter Jungk (BMFTR, virtual), and the presidents of our two universities, Prof. Thomas Hofmann (TUM) and Prof. Matthias Tschöp (LMU).
Inspiring keynote talks from Prof. Frank Fitzek (TU Dresden) and relAI Fellows Prof. Julia Schnabel (Helmholtz, TUM) and Prof. Stefan Feuerriegel (LMU) addressed reliability challenges in the areas of communication networks, medical imaging, and decision-making, respectively. Additionally, relAI Fellow Prof. Dr. Enkelejda Kasneci (TUM) introduced the exciting new relAI research area “Learning & Instruction,” which focuses specifically on AI in Education.
In a captivating panel discussion, Dr. Philipp Baaske (LMU, VP Entrepreneurship), relAI Fellow Prof. Claudia Eckert (acatech, President), Anna Kopp (Microsoft Digital Germany, CIO/CDO), and Maria Sievert (founder of inveox) exchanged views on balancing innovation, safety, and scale, highlighting their perspectives on the present and future of governance, certification, and ecosystem responsibility.
relAI students took the stage to share their experiences as members of relAI. Tzu-Yuan Huang, Amine Ketata, Sofiia Nikolenko, and Johanna Topalis talked about how relAI has influenced their careers and the opportunities it has provided for their professional development. In addition, our guests had the opportunity to engage in discussions with relAI students during the poster session, learning more about relAI research.
🙏 Thank you to all the speakers, moderator Petra Bindl, and relAI students for their contributions to the success of this event.
Don’t miss this photo gallery showcasing the best moments of the event!
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The annual meeting of the three German Zuse Schools - ELIZA, SECAI, and relAI - funded by DAAD in 2022, was hosted by ELIZA this October in the beautiful halls of Technische Universität Darmstadt. The meeting highlighted the Zuse School initiative´s success in attracting international AI talent and providing an exceptional, innovative education closely connected to the industry. Most importantly, it served as a wonderful "family gathering," reinforcing the strong ties that have developed among the three schools.
The program began with welcome addresses by Prof. Dr. Matthias Oechsner, Vice President for Research at TU Darmstadt, and Dr. Michael Harms, Deputy Secretary General of the DAAD, and was followed by intriguing keynote lectures from Prof. Dr. Markus Reichstein and Dr. Claudius Gläser, representing the academic and industry sectors, respectively. This was followed by many exciting presentations from our students showcasing their outstanding research results, including that of relAI PhD Student Valentine Idakwo. A panel discussion featured the participation of relAI Fellow Volker Tresp, and an interesting poster session encouraged interactions among members. The program was rounded off by an impressive, guided tour through robotics lab.
🙏Thank you, ELIZA, for the great organization of the event!
🙏 A heartfelt thanks to DAAD and BMFTR for supporting us! A great thanks also to our presidents, Matthias H. Tschöp and Thomas F. Hofmann, for their additional support and for creating such inspiring environments at Ludwig-Maximilians-Universität München and Technische Universität München for our Zuse School relAI!
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relAI is proud to have supported the KI-Symposium 2025, which took place yesterday at the historic Große Aula of Ludwig-Maximilians-Universität München. The event brought together researchers, students, and guests from academia, industry, and the public to celebrate the vibrant and diverse AI landscape at LMU, one of the two universities affiliated with relAI.
Throughout the evening the breadth, depth, and societal relevance of the research presented stood out. It's difficult to pick a single highlight from such a packed program. The event included forward-looking opening remarks by LMU Vice President for Digital Strategy, Prof. Dr. Julia Dittrich, and Dr. Christian Scharpf from the Landeshauptstadt München. Attendees enjoyed an insightful keynote by relAI Fellow Prof. Dr. Björn Ommer, an engaging panel on LMU's AI strategy featuring Vice Presidents Prof. Dr. Julia Dittrich, Dr. Philipp Baaske, and relAI Fellow Prof. Dr. Jochen Kuhn, a demonstration of PathoPan by Aicendence as part of the AI Transfer, pitch talks by LMU’s newly appointed AI professors, and the presentations of the AI-HUB@LMU Prize.
