Rebekka Burkholz is a full professor at the CISPA Helmholtz Center for Information Security in Saarbrücken, where she leads the relational machine learning group. Her main goal to develop efficient deep learning algorithms that are robust to noise, require small sample sizes, and are generally applicable in the sciences. Her work is founded in theory with implications for real world applications and is often characterized by a complex network science perspective. Her favourite applications and sources of inspiration are currently the biomedical domain, pharmacy, and physics. Her group is supported by the ERC starting grant SPARSE-ML. 

iří Matas is a Professor at the Center for Machine Perception at the Czech Technical University in Prague. His research focuses on a wide range of topics in computer vision, including object detection and tracking, visual matching, scene text recognition, and robust model estimation. His work combines theoretical insights with practical applications and has significantly influenced both academic research and real-world systems. He has received several best paper awards at leading conferences like CVPR and ECCV and has served as Program Chair for both CVPR and ECCV. 

Dorit Merhof is Professor and Chair of Image Analysis and Computer Vision at the University of Regensburg. Her research focuses on the development of advanced computer vision and machine learning techniques for biomedical image analysis, with applications ranging from digital pathology and neuroimaging to cell and tissue segmentation. Her work bridges foundational AI research and real-world medical applications, supporting clinical decision-making through interpretable and robust algorithms. Prof. Merhof previously held a professorship at RWTH Aachen and actively collaborates across disciplines in medicine, biology, and data science.