During its recent meeting, the L3S Scientific Advisory Board discussed the institute’s progress, future research priorities, and the evolving role of L3S within the European AI ecosystem.
Physics-informed neural networks can make black-box models more reliable. The result is versatile surrogate models for complex applications, such as the precise control of soft robots.
Neural Attention Search (NAtS) enables AI to focus on what truly matters — significantly reducing energy and hardware costs while maintaining performance.
Fed-FUEL enables fair, powerful and privacy-compliant AI in federated learning – without adapting the underlying models and with a special focus on protecting disadvantaged groups.
With its 40 finely graded emotional states and balanced dataset, EmoNet Face bridges the gap between 'emotionally blind' AI and the emotional perception of human experts.
DeepCAVE provides insights into complex AutoML optimisations, helping users understand key parameters, performance dynamics, and opportunities for improvement.
HyperSHAP reveals which hyperparameters truly matter and how they interact, bringing new transparency and efficiency to the optimisation of modern AI systems.
A combination of a pulsating fluid jet, audio monitoring and AI enables the safe and minimally invasive removal of bone cement – an important component for gentle, automatable revision surgery.
At the 10th L3S Town Hall Meeting on 6 February 2026, outstanding research results took centre stage alongside current developments and new projects. Prof. Dr. […]
Two researchers from the L3S Research Center have been awarded ERC Proof of Concept Grants: Professor Sören Auer and Professor Marius Lindauer will receive funding […]
