This recognition honors his pioneering work on Stable Diffusion, a generative AI model that has democratized image generation. Stable Diffusion enables users to create high-quality images from text descriptions, making advanced image generation accessible through open-source software without the need for expensive hardware. This innovation exemplifies our mission at relAI, which focuses on advancing reliable AI systems.
The German AI Award, presented by WELT, recognizes innovations that drive the future of AI both in theory and application. Björn Ommer’s recognition reflects his significant role in shaping the evolution of reliable and transparent AI systems.
relAI is proud to announce the addition of five exceptional fellows to the school. Enkelejda Kasneci (TUM), Gjergji Kasneci (TUM), Björn Ommer (LMU), Tom Sterkenburg (LMU), and Abdalla Swikir (TUM) have each made significant contributions to their respective fields and bring a wealth of knowledge and experience to our family.
The research topics of the new fellows tackle essential aspects of the field of reliable AI. Their work ranges from the study of human-computer interaction and semantic scene understanding to the study of fairness and inductive bias in machine learning as well as safe learning in robotics. Their engagement in the school's research and educational activities will contribute to the reliable application of AI in real-life scenarios, such as improving user experience in digital interfaces and enhancing the safety of autonomous systems.
Enkelejda Kasneci is a Distinguished Professor (“Liesel Beckmann Distinguished Professorship”) for Human-Centered Technologies for Learning at the TUM School of Social Sciences & Technology. Her research focuses on Human-Computer Interaction and developing AI systems that sense and infer the user’s cognitive state, expertise, actions, and intentions based on multimodal data.
Gjergji Kasneci holds the Chair for Responsible Data Science at TUM School of Computation, Information & Technology. His research focuses on transparency, robustness, bias, and fairness in machine learning algorithms, incorporating ethical, legal, and societal considerations.
Abdalla Swikir is a Senior Scientist and Teaching Coordinator at the TUM Munich Institute of Robotics and Machine Intelligence (MIRMI). His research in safe learning for robotic control and autonomous systems targets the enhancement of reliability and safety, ensuring these technologies can effectively function in dynamic and critical environments.
Björn Ommer is Head of the LMU Computer Vision & Learning Group. His research interests include semantic scene understanding and retrieval, generative AI and visual synthesis, explainable AI, and self-supervised metric and representation learning. Moreover, he is applying this basic research in interdisciplinary projects within neuroscience and the digital humanities.
Tom Sterkenburg is an Emmy Noether junior research group leader at the Munich Center for Mathematical Philosophy at LMU Munich. His Emmy Noether project, “From Bias to Knowledge: The Epistemology of Machine Learning", is concerned with clarifying the fundamental concept of inductive bias in machine learning.
“For his impressive track-record and contributions in the field of dependable systems, including multiple publications in highly regarded venues, and influence on practical dependable systems.”
The award aims to recognize a junior researcher, “who demonstrates outstanding potential for creative ideas and innovative research in the field of dependable and resilient computer systems and networks”.
The German National Scholarship Foundation (Studienstiftung) has awarded Maria the Civic Engagement Award 2024 for her exceptional volunteering work with Lern-Fair. Maria co-founded and chairs Lern-Fair e.V., a non-profit organization dedicated to providing free educational opportunities for underprivileged pupils. Since the start of the online platform in 2020 during the Covid pandemic, more than 15.000 pupils were supported by free tutoring or group courses.
Maria is a relAI PhD student at the chair for Mathematical Foundations of Artificial Intelligence at LMU and the Munich Center for Machine Learning. Her PhD research, advised by the relAI director Gitta Kutyniok, focuses on the mathematical description and understanding of training dynamics related to generalization, a crucial factor for ensuring the reliability of neural networks.
Each of them brings their own expertise and insights that will further enrich our research agenda, educational offers, and scientific community. They are dedicated to making significant contributions to the advancement of reliable AI, particularly in Medicine & Healthcare and AI in Education areas.
Nassir Navab is a full professor and director of the Laboratories for Computer Aided Medical Procedures at TUM and adjunct professor at John Hopkins University. One focus of his research is AI assisted Surgery, where reliable methods are a key requirement for both clinicians and patients.
Solveig Vieluf is a professor of AI-based telemonitoring in the field of cardiology at LMU. Previously, she has also worked on epilepsy and aging research. She uses methods from explainability to explore influence factors on model performance.
The research of Tobias Lasser is focused on computational imaging and inverse problems in medicine and healthcare. In his work on clinical decision support using AI, he works on prioritization of critical cases for treatment.
Jochen Kuhn works on the intersection of AI and education, in particular on the use of these future technologies to foster learning and teaching in STEM disciplines. He is a professor of Physics education at LMU. Reliability is important in his research, particularly the role of bias and inaccurate information from AI chatbots on learning and teaching.
Join us in welcoming these four to the relAI community!
Julia Schnabel is Professor for Computational Imaging and AI in Medicine at the Technical University of Munich TUM (Liesel Beckmann Distinguished Professorship), and Director at the Institute for Machine Learning in Biomedical Imaging at Helmholtz Munich (Helmholtz Distinguished Professorship). Since 2015, she has also been Professor of Computational Imaging at King's College London.
Prof. Schnabel works in the field of medical image processing and machine learning. Her research focuses on the areas of intelligent imaging up to clinical evaluation, including complex motion modeling, image reconstruction, quality assurance, segmentation, and classification applied to multimodal, quantitative, and dynamic imaging.
Congratulations!
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Are you interested in frontier AI systems, their astonishing capabilities and risks for humanity? Then join us for a thought-provoking deep dive and exclusive OpenAI Live Q&A on AI safety.
Date: Wednesday, May 8th, 2024 | 19:00 – 20:30
Location: Room B006, Department of Mathematics (Theresienstr. 39) or online
Language: English
Agenda:
19:00 – 19:05: Doors open
19:05 – 19:30: Introduction to AI Safety
19:30 – 20:15: Presentation & Live Q&A with OpenAI researcher Jan H. Kirchner, co-author of weak-to-strong generalization paper
20:15 – 20:30: Closing talk – What can we do?
20:30 – onward: Optional socializing and small group discussions with free drinks and snacks.
Please register on the following webpage and prepare your questions!
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Last week, our relAI students presented their research to the relAI industry partners in a series of industry workshops. Four events took place, each centered around one of the four relAI’s research areas: Mathematical & Algorithmic foundations, Algorithmic Decision-Making, Medicine & Healthcare and Robotics & Interacting Systems.
We are thrilled that this event was so well received both by the students and the industry partners! Following short lightning talks, intriguing discussions around reliability of AI took place in smaller breakout groups.
The industry workshops are part of relAI´s cross-sectional training and aim to facilitate the exchange of insights and expertise between academia and industry. The engagement from both our students and industry fellows emphasized the significance of bridging academic excellence with real-world applications, particularly when addressing the evolving challenges in AI reliability.
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We are excited to announce that our call for applications to the relAI MSc program is now open!
The novel, innovative relAI MSc program is an addition to a regular MSc program at Technical University of Munich (TUM) or Ludwig Maximilians University (LMU), offering comprehensive cross-sectional training in reliable AI, including scientific knowledge, professional development courses, and industrial exposure. Funded applicants receive a scholarship of up to 934€ and additional support such as travel grants for home travel.
relAI, funded by the German Academic Exchange Service (DAAD), is embedded in the unique transdisciplinary Munich AI ecosystem, combining the expertise of the two Universities of Excellence TUM and LMU of Munich.
We highly encourage you to apply if you:
hold an excellent Bachelor’s degree in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines,
are accepted to a MSc program in said disciplines at either TUM or LMU starting in spring or fall 2024, or have applied there (Acceptance necessary before joining relAI)
have a genuine interest to study 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 or Robotics & Interacting Systems, and
can certify proficiency in English on C1 or higher level.
We are thrilled to announce our new industry partnership with SAP!
The new collaboration will strengthen the school's expertise in Business AI, and will contribute to translate our AI research into the development of reliable AI systems. You can read below the views of SAP members on this exciting alliance between relAI and SAP.
We are thrilled to extend our collaborative efforts on research-driven product innovation with the Technical University of Munich and the Munich ecosystem through our new partnership with the Konrad-Zuse-School of Excellence in Reliable AI. This expansion not only consolidates our portfolio of AI-related applied research projects, but also fosters a more profound knowledge exchange and talent engagement on topics around the development of reliable AI systems and Business AI.
- Dr. Katharina Wollenberg and Dr. Rüdiger Eichin, Industry-University Collaboration, SAP
At SAP, we are committed to help our customers leverage AI to create tremendous business value. We specialize in Business AI: AI that is relevant since it’s embedded in enterprise business applications and processes from day one; that is reliable since we train, ground, and adapt AI on companies’ business data and context; and that is responsible by design, following SAP’s rigorous AI ethics, privacy, and security practices. We are delighted to join the Konrad Zuse School of Excellence in Reliable AI and look forward to collaborating with academica to drive the development and delivery of relevant, reliable, responsible Business AI.
