👋 relAI warmly welcomes our new Fellow, Björn Eskofier. He has recently joined LMU, focusing on AI-supported therapy decisions. Previously, he held the Chair of Machine Learning and Data Analytics at Friedrich-Alexander University Erlangen-Nuremberg. His research aligns with the central themes of relAI, namely safety, security, privacy, and responsibility within the Medicine and Healthcare relAI research area 🩺.
As a relAI Fellow, he will contribute to the relAI Curriculum through lectures, mentoring, supporting relAI students, and participating in relAI events.
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📢 relAI is excited to announce that LMU Professor Falk Schwendicke has joined our school.
About Professor Falk Schwendicke
Professor Schwendicke is Director of the Poliklinik für Zahnerhaltung, Parodontologie und digitale Zahnmedizin at the LMU Klinikum. He brings extensive experience in applying and evaluating AI solutions in dental diagnostics, clinical decision-making, and public health. His research aligns perfectly with relAI’s focus on developing reliable, trustworthy, and human-centered AI. He develops and evaluates AI models for clinical settings, where performance, interpretability, and safety are critical. His work emphasizes important topics such as multimodal learning, explainability, fairness, and generalizability. This includes benchmarking algorithms and addressing dataset biases.
Contribution to relAI
As a relAI fellow, Professor Schwendicke will actively contribute through lectures, seminars, mentoring students, and supporting various relAI events.
Ecologic-Computing GmbH develops software that enhances communication, storage, and computing efficiency. The company is currently focusing on beyond-Shannon communication, which explores alternative models of information processing that prioritize semantic relevance, efficiency, and robustness. Additionally, Ecologic-Computing is researching alternative computing approaches, including analog, beyond-digital, and biologically inspired computation.
Partnership with relAI
Ecologic-Computing’s research will contribute to the mathematical and algorithmic foundations relAI research area, with a focus on robustness, efficiency, and the design of responsible systems. Additionally, Ecologic-Computing will create opportunities for relAI students to collaborate and exchange ideas, giving them valuable hands-on research experience. By offering guest lectures and seminars on topics such as entrepreneurship, technology transfer, and its research and development roadmap, Ecologic-Computing will equip relAI students with insights into translating research into industry applications.
Ecologic-Computing anticipates that close interaction with relAI will benefit both researchers and students, providing access to valuable theoretical expertise.
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🎉 Congratulations to our relAI Fellows Daniel Rueckert and Fabian Theis 💐
Google.org, the philanthropic arm of Google, has announced the twelve recipients for its $20 million AI for Science fund. This initiative aims to accelerate research in health, agriculture, biodiversity, and climate.
The Technische Universität München is among the organizations that received funding to advance health research. TUM Professors and relAI Fellows Daniel Rückert and Fabian Theis will developa multiscale foundation model connecting individual cells to entire organs. This model will let clinicians simulate disease progression and evaluate potential treatments in a digital environment.
We look forward to following the project's development!
On January 20, 2026, researchers from relAI, MDSI and our industry partner SAP gathered for a pitchtalk session at the SAP Labs Munich Campus in Garching. In an interactive setup, participants from the three organizations exchanged research insights and explored ideas for future collaborations.
The afternoon opened with short welcome words from representatives of SAP (Dr. Tobias Müller), MDSI (Sylvia Kortüm) and relAI (Dr. Mónica Campillos). The following pitchtalks covered a wide range of topics relevant to AI and Data Science, spanning methodological approaches to practical research resources.
Pitchtalk Session
The talks introduced methodologies and applications across various research fields, from uncertainty modeling to personalized medicine and research data infrastructure.
Max Beier, a relAI PhD student, discussed the advantage of using linear-operator methods in dynamical systems. By focusing on linear operators, his approach supports optimal control, scalable optimization algorithms, and reliable forecasting across time scales ranging from milliseconds to days. He showed that this methodology allows efficient learning of system behavior, modeling of trajectory distributions, and generation of coherent sequences, addressing current limitations in decision‑making models for complex dynamical environments.
Parastoo Pashmchi, an industrial PhD student at SAP, introduced an efficient algorithm to handle missing data, a common challenge in AI projects. She presented an ML‑based imputation method that preserves the original data distribution by sampling from the conditional distribution of nearest neighbors, overcoming the limitations of common techniques like kNNImputer. By enabling uncertainty quantification and multiple imputations, her approach improves the reliability of predictive models such as SAP’s green energy forecasting use case, where missing solar production data can significantly undermine model accuracy.
Mario Picciani, an MDSI PhD student, highlighted recent developments and applications of ProteomicsDB, a proteomics resource established in 2012 through a collaboration between MDSI Core Member Prof. Bernhard Küster and SAP. ProteomicsDB is a powerful multi‑omics, multi‑organism platform that enables real‑time exploration of proteomic, transcriptomic, and drug‑interaction data across the tree of life, supporting research from basic biology to large‑scale initiatives. With expanding capabilities for analyzing drug mechanisms, predicting cell responses, and supporting precision oncology, the SAP HANA–powered resource could become a central tool for biomarker discovery, systems biology, and personalized medicine.
Sebastian Gallenmüller, an MDSI PhD student, presented SLICES-DE, a digital research infrastructure for computing and communication, embedded within a European collaborative network. As a national digital research infrastructure for ICT, SLICES‑DE offers remote‑accessible testbeds, reproducible workflows, and long‑term data management to support research in areas such as 6G, AI, cybersecurity, and cloud‑edge systems. Built as a community‑driven, flexible, and scalable platform, it enables shared experiments, training, and industry collaboration, providing both academia and companies with a versatile environment that can even be booked for individual lectures or large‑scale projects.
Networking over pretzels and lemonade
An informal networking session after the pitchtalks gave speakers and participants from relAI, MDSI, and SAP the opportunity to connect, exchange impressions, discuss research interests, and explore potential collaborations.
We thank our industry partner SAP for hosting this event and sharing insights into ongoing research projects – we look forward to future editions!
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.
