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:
We are pleased to share relAI’s contribution to the European Embodied Robotics Week, whichtook place last week in Munich. The event was organizedby RoboTUM together with START Munich and the ESRA (European Student Robotics Association) network.
The Robotics Festival convened a diverse range of stakeholders from the European robotics and physical artificial intelligence ecosystem. Specialists, students, and robotics enthusiasts participated in city-wide events, including open houses at robotics laboratories, makerspaces, and studios throughout Munich, as well as a hackathon.
The event ended with a summit featuring talks and panels from leaders in embodied intelligence. relAI was represented in the panel discussion “Dexterity and Manipulation for robotics” by relAI Fellow Prof. Khadiv.
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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."
The Berlin-Brandenburg Academy of Sciences and Humanities has a remarkable 325-year history of bringing together brilliant scholars and scientists from various backgrounds and disciplines, including 82 Nobel Prize winners. This Academy plays a vital role in our society by not only advancing research in the humanities but also addressing critical scientific and social issues through collaboration. We deeply value its mission to foster understanding and dialogue between the scientific community and the broader society, as this connection is essential for tackling the challenges we face together.
🎉 Congratulations!
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On June 8, 2026, TUM hosted the International Symposium 'Future Learning: Global Perspectives, European Pathways' as part of the new relAI research area Learning & Instruction.
As our current Coordinator is taking an exciting 👏 next step in their academic career to transition into a professorship 🎓, a wonderful opportunity has opened to join our team.
If you are interested in working at our innovative and interdisciplinary school, where you can help shape the research and education of the next generation of experts in reliable AI, we encourage you to check out the job posting.
If you meet the qualifications and would like to apply, please submit your application by June 14, 2026.
Coordinator of the Konrad Zuse School of Excellence in Reliable Artificial Intelligence (m/f/d)
The Konrad Zuse School of Excellence in Reliable Artificial Intelligence (relAI), founded in 2022, is one of three DAAD funded AI schools in Germany. relAI is a joint endeavor of TUM and LMU and has quickly become an international lighthouse for education and research in reliable AI in Germany. More than 100 MSc and doctoral researchers participate simultaneously, supervised by more than 50 professors. Our network includes international AI centers, non-university research organizations and various industrial partners. relAI opens up career paths in academia and industry to talented young researchers from around the world. The coordination office of this graduate school is located at and embedded in the infrastructure of the Munich Data Science Institute (MDSI) – an Integrative Research Institute at the Technical University of Munich (TUM) with an interdisciplinary and cross-faculty focus in the field of data science, machine learning and A
As the Coordinator of relAI you will be responsible for managing school-wide and extra-school activities.
Key responsibilities:
Developing and organizing events and training courses, e. g. career fairs, seminars, symposia, workshops, seasonal schools, retreats and conferences
Reporting to the directors, the DAAD and various stakeholders
Serving as the primary point of contact for our fellows and for our academic and industrial partners
Developing and executing recruitment strategies for the school´s candidates
Overall responsibility for the school's budget monitoring and financial reporting in coordination with TUM’s central administration; overseeing the allocation of funds and maintaining the comprehensive records and databases of the relAI network
Implementation and maintenance of efficient project management structures to ensure smooth daily operations of the graduate program.
Coordination of the application for project continuation after 2027.
Your qualification:
We are looking for a reliable and collaborative person able to manage a broad range of tasks and responsibilities with
Postgraduate degree (MSc or equivalent), combined with a certain affinity for data science, machine learning and AI
Professional experience in project management and/or science management, ideally in coordinating university programs or graduate schools
Leadership skills and a good intercultural understanding, high motivation, flexibility and reliability
Fluent in German and English, written and spoken, negotiating skills
Beyond standard MS Office, experience in managing digital collaboration tools and technical infrastructures is highly desirable (e.g., mailing lists, basic SQL databases, or content management systems for website maintenance).
We offer:
Full responsibility from the start in an innovative environment and with real impact on the data science ecosystem at TUM and in Munich
International, attractive, and interdisciplinary working environment across different departments and disciplines
Salary according to TV-L (depending on qualification up to E13) including social benefits.
Application:
The position can start immediately, and the contract duration is initially limited until December 31, 2027; a further extension is envisioned, subject to continued project funding. As an equal opportunity and affirmative action employer, TUM explicitly encourages nominations of and applications from women as well as from all others who would bring additional diversity dimensions to the university’s research and teaching strategies. Preference will be given to disabled candidates with essentially the same qualifications. If you are interested, please apply until June 14, 2026 with a cover letter (stating also your earliest date of entry), CV and relevant certificates plus reference letters via email to application@mdsi.tum.de (please attach a single PDF file and use the subject Coordinator relAI).
As part of your application for a position at the Technical University of Munich (TUM), you are transmitting personal data. Please note our data protection information in accordance to Art.13 General Data Protection Regulation (GDPR) for the collection and processing of personal data in the context of your application (https://portal.mytum.de/kompass/datenschutz/Bewerbung/). By submitting your application, you confirm that you have taken note of TUM´s data protection information.
Contact: https://zuseschoolrelai.de/ Email: coordinators@zuseschoolrelai.de Technische Universität München Munich Data Science Institute Konrad Zuse School of Excellence in Reliable AI Walther-von-Dyck-Straße 10 85748 Garching bei München
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🔮How will artificial intelligence (AI) influence the future of learning?
📢 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;
