Fruitful scientific interaction between relAI, MDSI and SAP

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!

👉 Photo Gallery

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

      LMU room finder: https://www.lmu.de/raumfinder/#/

📅 Thursday, February 12, 2026

🕡 5:00 pm – 6:30 pm

About the Lecture

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.

Follow this link for more information.

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)

👉Registration: here 

ℹ️ More information: Questions? Please contact iuc-tum(at)sap.com.

Confirmed speakers: 

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’.

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!

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!  

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.

Don’t miss the upcoming  Munich AI Lecture featuring Prof. Yoshua Bengio from Université de Montréal.

As AI capabilities accelerate, a critical question emerges: can we ensure these systems remain aligned with human values? While advances in reasoning and planning bring us closer to broadly human-level intelligence, recent findings also reveal troubling behaviors such as deception, hacking, and resistance to shutdown.

In his Munich AI Lecture, Yoshua Bengio will explore these challenges and outline a safer path forward. He argues for the design of non-agentic yet trustworthy AIs — systems modeled after a selfless scientist, dedicated to understanding the world rather than pursuing their own goals. Such “Scientist AIs” could act as monitors, helping society manage more powerful agentic systems and reduce existential risks. Beyond technical solutions, Bengio calls for political coordination at national and international levels, treating transformative AI as a global public good essential for safeguarding democracy and stability.

The lecture will be followed by a panel discussion, including panelists Andrea Martin (Chief Technology Officer, IBM), relAI Director Prof. Dr. Gitta Kutyniok, and Stephanie Jacobs (Head of Office, Bavarian State Ministry of Science and the Arts), which will be moderated by Dr. Michael Klimke (CEO, BAIOSPHERE).

📍 Bavarian Academy of Sciences Alfons-Goppel-Str. 11 (Residenz) 80539 Munich

📅 23 October 2025  

🕡 6:00 pm - 9:00 pm

About the Speaker

Yoshua Bengio is Full Professor at Université de Montréal, Founder and Scientific Advisor of Mila, Co-President and Scientific Director of LawZero, and Canada CIFAR AI Chair. A recipient of the 2018 A.M. Turing Award — often called the “Nobel Prize of computing” — he is the most cited computer scientist worldwide and among the most cited living scientists across all fields. Bengio is a Fellow of the Royal Society of London and Canada, an Officer of the Order of Canada, a Knight of the French Legion of Honor, and currently chairs the International AI Safety Report.

Registration

The event is already fully booked. If you would like to get put on the waiting list, please send  a message: events@baiosphere.org

The lecture will also be available via LIVESTREAM on YouTube (no registration needed).

More information:

https://baiosphere.org/en/events/2025/munich-ai-lecture-prof-yoshua-bengio

https://www.lmu.de/ai-hub/en/news-events/all-events/event/munich-ai-lecture-prof.-yoshua-bengio.html

Last weekend, relAI engaged with children during the TUM Open Doors with the Mouse 2025 event at the Munich Data Science Institute (MDSI). Students from relAI - Manuel Hülskamp, Natascha Niessen, Lisa Schmierer, and Richard Schwank - enthusiastically participated, using computer games to explain concepts of machine learning and artificial intelligence to the young attendees. The children learned how to train an AI model to differentiate between apples, pears, bananas, and plums, and they even had the opportunity to recognize their own faces, testing this feature with their siblings and other visitors.

A heartfelt thank you to the relAI students and coordinator Andrea Schafferhans for their support of the event, as well as to the families who participated.

Check the MDSI news for extensive information about the event.

Registration for the Munich Career Fair AI & Data Science 2025 is now open! You can request your ticket here.

📅 Date and Time: October 23, 2025, 2 to 5 pm

📍 Location: TranslaTUM at Klinikum rechts der Isar, Einsteinstraße 25 (Bau 522), 81675 Munich

🏢Participating companies: Celonis, DENSO, Diehl, GE Healthcare, Google, Imfusion, Munich Re, QuantCo, SAP, Thyssenkrupp, and Zeiss

👉 Check out this link for more information and the agenda

Don’t miss the upcoming  Munich AI Lecture featuring Prof. Aaron M. Johnson fromCarnegie Mellon University and Visiting Professor at TUM.

What happens when robots leave the lab and enter the real world? Suddenly, uncertainty is everywhere — slippery mud, bending branches, unpredictable terrain. These challenges are especially tough when it comes to contact: one moment a robot applies massive force, the next it has no grip at all.

In his talk, Aaron M. Johnson shows how robots can learn to master the unknown — from off-road driving in new environments to agile walking through vegetation. Expect cutting-edge insights into how uncertainty can be modeled, reduced, and even turned into an advantage for the future of robotics.

📍 Georg-Brauchle-Ring 58, Room M001
The TUM Room finder will help you find the way

More information: