🙌 We warmly welcome Valentin Hofmann, an incoming tenure-track assistant professor at LMU Munich in Information and Language Processing using AI methods.
Valentin Hofmann's research lies at the intersection of AI, natural language processing, and computational social science. A primary focus of his work is to enhance the robustness, safety, and fairness of large language models, particularly regarding social biases and their implications for reliable AI.
His studies on large language models are relevant to the relAI Research Area of 🤖 Robotics and Interactive Systems, as these models increasingly serve as essential components of interactive, human-facing AI systems, such as conversational assistants, where reliability is crucial. Furthermore, his research directly aligns with the relAI Central Themes of Safety and Responsibility by investigating and mitigating social biases and their potential risks in deployed AI language technologies. As a fellow, he will contribute to relAI through teaching, mentoring, and community activities.
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👏 Congratulations to relAI PhD Student Jan Simson and relAI Fellow Prof. Christoph Kern!
The German Society for Online Research (DGOF) annually recognizes outstanding scientific contributions to the advancement of the methods of online research through the DGOF Best Paper Award.
Their award-winning paper emphasizes the importance of transparency and accessibility in key decisions throughout the machine learning pipeline for the general public. It introduces a participatory approach to help navigate the multiverse of design choices, advocating for the democratization of essential decisions rather than simply focusing on optimization.
📖 To the article:
Simson, J., et al. (2025). Preventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 806, 1-30. https://doi.org/10.1145/3706598.3713482
His key research interests are focused on ultra-efficient AI chips, advanced technologies, novel computing architectures for AI acceleration, machine learning for EDA, advanced technologies, cryogenic CMOS, emerging beyond- CMOS technologies, privacy and security. His research aligns strongly and naturally with the core mission of relAI, particularly its central themes of security, privacy, and reliability, as well as its application-driven focus on trustworthy AI systems. While relAI emphasizes algorithmic and theoretical foundations, Prof. Amrouch contributes a complementary and essential hardware-level perspective, addressing reliability not only at the software or model level, but at the physical, architectural, and system layers of AI.
Contribution to relAI
Prof. Hussam Amrouch is committed to making sustained contributions to the relAI program through research supervision, teaching, training, and community-building. His involvement will strengthen relAI’s interdisciplinary profile by integrating hardware-aware, security-focused, and energy-efficient AI perspectives into both doctoral education and research.
Zeynep Akata is a Liesel Beckmann Distinguished Professor of Computer Science at TUM and the Director of the Institute for Explainable Machine Learning at Helmholtz Munich.
Her research focuses on explainability-guided model adaptation, bias detection and mitigation, mechanistic interpretability, and continual learning—all of which are central challenges for reliable AI. Methodologically, her work spans representation learning, interpretability diagnostics, model consolidation, and evaluation under distribution shift 📈, with close connections to real-world applications in medical imaging and clinical decision support.
Contribution to relAI
As a relAI Fellow, she will contribute to the relAI Curriculum through lectures, mentoring students, supporting relAI events, and participating in strategic discussions on evaluation standards and benchmarks for reliable AI.
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👋 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!
