From July 28 to July 31, 2026, relAI hosted the second relAI International Summer School at LMU. This event focused on promoting educational exchange aimed at developing reliable AI and exploring current trends in the field.
Last week, our industry partner, QuantCo, hosted a networking event with relAI at their new Munich office.
The relAI students had a wonderful evening filled with engaging conversations and delicious food. They had the opportunity to meet colleagues from QuantCo's AI, and engineering teams and learned about how the company develops scalable systems to transform messy, real-world data into impactful decisions.
The summer school took place in Bologna from June 9 to June 17, 2026, and was a fantastic opportunity for young PhD students and researchers to explore the exciting connections between imaging sciences and machine/deep learning.
In her poster, Inés introduced CHEM (Conformal Hallucination Estimation Metric), a method for detecting and quantifying hallucinations in image reconstruction models. By combining wavelet and shearlet representations with conformalized quantile regression, CHEM identifies realistic-looking artifacts that aren’t present in the ground truth in a way that doesn’t depend on distribution.
We are happy to announce that relAI will be represented in Seoul, South Korea, from July 6 to 11 at the Forty-Third International Conference on Machine Learning (ICML2026)!. ICML is widely recognized as one of the top three most influential conferences in machine learning and artificial intelligence research.
📖 About relAI Publications at ICML
relAI contributes fourteen publications to the conference, including nine in the main track.
relAI PhD student Emre Kavakwill present a Spotlight paper (ranked in the top 2.2%).
Five relAI papers will be presented at ICML Workshops, with contributions from three relAI MSc students.
🤝Meet relAI Students
If you are attending ICML we encourage you to engage in discussions about relAI research with the following relAI students present at the conference:
relAI is proud to announce an outstanding achievement – a first author publication of Moritz Knolle, one of relAI’s PhD students, in Nature journal.
Medical AI models are increasingly utilized in applications such as diagnosing and remotely treating patients. While these models have proven valuable to both practitioners and patients, concerns remain about the privacy of patients whose information is used to train these AI systems. This issue has been examined by relAI PhD student Moritz Knolle, in a study published this week in Nature. The analysis revealed privacy vulnerabilities in medical AI models, emphasizing the importance of reliable AI research in medicine and healthcare 🩺.
🔍 Check the summary below and the article to learn more about the study!.
Summary of the article
Individuals whose data are used to train medical AI models may be at risk of being identified in cyber-attacks, according to a Nature paper published this week. Underrepresented groups may face disproportionately higher risks of having their data compromised, the study indicates. The researchers find these individuals are not accounted for in current risk assessments and call for further mitigation and strict access control.
Medical AI models may improve global health outcomes, especially in areas in which specialized expertise is not available. Yet, the sensitive data used to train these models may be exposed to privacy attacks. Membership inference attacks (MIAs) are used by attackers to determine whether an individual’s data were used to train a model. From these attacks, a patient’s medical data and private information can be determined. Previous research on data risk has been determined by whole datasets, and does not take an individual’s risk into account.
Moritz Knolle and colleagues conduct a privacy audit to focus on individual privacy risk, finding that medical AI models may pose a privacy risk to individual data contributors. Using seven large datasets made up of real-world clinical data, including medical images, electrocardiograms and electronic health records, the authors determine the most vulnerable among data-contributing patients. They find that at an individual level, those targeted by the MIAs were successfully done so with almost no error. At a group level, those identified as underrepresented in datasets include people with rare diseases, people from a minority racial group or, socioeconomic status, or those having the less-common gender. With more distinctive data that are encoded by AI models, these groups and individuals are found to be more vulnerable and disproportionately exposed to privacy attacks. The authors find the instances of successful MIAs attacks rise with model capacity and size.
These findings show privacy attacks, such as MIA, are more effective at successfully targeting on an individual level than currently thought. The authors conclude that privacy risk assessment must now take individual risk into account, and vulnerable models be further protected."
Frontier AI systems have recently solved IMO problems, discovered new mathematical constructions, and resolved open Erdős problems. Yet a more mundane question remains: 🤔how should a working mathematician actually use these tools day to day?
In this talk, titled “The Agentic Researcher: Turning AI Coding Agents into Research Assistants” Emil Partow, PhD Student of our relAI Director Prof. Gitta Kutyniok presented a recent paper from Prof. Pokutta (Zuse-Institut Berlin) that offers a concrete answer. The authors propose a five-level taxonomy of AI integration into research, ranging from classical work without AI to fully autonomous research loops. They have implemented this idea within an open-source framework.
Following a detailed presentation by Emil Partow, members of relAI and Prof. Kutyniok's research group gathered to discuss this forward-looking topic. The presentation explained the open-source tool that implements core research "commandments" (such as preventing the falsification of experimental data) via a practical, actionable loop. Participants then discussed how AI agents are already shaping research methodologies, what is required to implement these workflows successfully, and how to ensure human oversight remains at the center of the process. Emil also shared a practical case study demonstrating the tool in action, sparking a broader reflection on the evolving role of AI in modern research.
/ mc
📢 We are excited to announce that the call for applications to the MSc program 2026 of our Konrad Zuse School of Excellence in Reliable AI (relAI) is now open!
The innovative relAI MSc program is an addition to the MSc program at TUM or LMU, offering a cross-sectional training for successful education in AI. It provides a coherent, yet flexible and personalized training by enhancing scientific knowledge, professional development courses, and industrial exposure.
Funded applicants will receive a scholarship of up to 992 EUR (depending on independent income). They are further supported by travel grants, e.g., for home travel.
We highly encourage you to apply if you have:
an excellent bachelor’s degree in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines;
Please help us in spreading the word, especially to excellent international candidates.
/ mc
relAI research will be featured at the International Conference on Learning Representations (ICLR), which will take place this year at the Riocentro Convention and Event Center in Rio de Janeiro, Brazil, from April 23rd to 27th, 2026. ICLR is one of the leading conferences with significant impact and reputation in machine learning and artificial intelligence research.
relAI Publications at ICLR
Seventeen publications from relAI will be presented at the conference, fifteen of them in the main track.
🎉 relAI is proud to announce that the International Association for Dental, Oral, and Craniofacial Research (IADR) has named relAI Fellow Falk Schwendicke as the recipient of the 2026 IADR Distinguished Scientist William H. Bowen Research in Dental Caries Award 🦷 .
IADR is a nonprofit organization dedicated to advancing dental, oral, and craniofacial research for global health and well-being. This esteemed IADR award recognizes exceptional and innovative contributions to our understanding of caries etiology and the prevention of dental caries. It is one of the 17 IADR Distinguished Scientist Awards and is considered one of the highest honors bestowed by the organization.
Falk Schwendicke’s Research Achievements
His early work focused on minimally invasive and evidence-based caries management, particularly regarding selective carious tissue removal and its economic evaluation. This research has laid the groundwork for contemporary treatment guidelines. His recent studies have increasingly emphasized the integration of emerging technologies to overcome challenges in caries detection and management. Notably, he has been a pioneer in employing advanced artificial intelligence (AI) applications for radiographic analysis, diagnostic support, and predictive modeling.
A significant achievement in his career was leading a randomized controlled trial that evaluated AI-assisted caries detection. This study set new standards for clinical research in the field and informed subsequent cost-effectiveness analyses. Schwendicke also participates in numerous editorial and review roles and has presented at the IADR General Session and various scientific meetings. He has authored over 500 peer-reviewed publications and 30 book chapters and is ranked among the top 1% of most-cited dental researchers worldwide, according to the Stanford global ranking.
📖 The article presents the first exploratory case study evaluating the quality of simplified radiology reports generated by the large language model (LLM) ChatGPT. Radiologists rated the reports as generally high quality but also identified errors that could lead to harmful patient interpretations. The findings highlight both the potential and the limitations of early large language models in clinical communication: while simplified reports can enhance accessibility, medical expert supervision and domain-specific adaptation are vital to ensure patient safety.
💡 The study, first published as a preprint in December 2022, was among the earliest scientific assessments of ChatGPT's ability to simplify radiology reports for patients. Since then, a rapidly growing body of research has explored the role of large language models in medical text simplification.
