ML: Bio-inspired Learning

Bio-inspired Learning explores computational models and algorithms inspired by biological systems, such as neural networks, evolution, and swarm intelligence. This approach aims to create efficient, adaptable, and intelligent systems by mimicking the problem-solving strategies of nature, advancing fields like AI, robotics, and optimization.

ML: Probabilistic Methods

This groups tackles challenges in safety, infrastructure, and sustainability. From emergency detection in households to clean water prediction, electric grid optimization, and real-time health monitoring, they combine innovative computational methods with practical applications. Together, they aim to deliver impactful solutions for pressing societal and environmental needs.

ML: Deep Generative Models & Autoencoders

This group focuses on designing AI systems that learn to generate and reconstruct data, uncovering complex patterns and structures. These models are vital for advancements in data compression, anomaly detection, and creative AI applications, driving innovation in fields like image synthesis, language generation, and scientific simulations.

ROB: Human-Robot Interaction

This group explores the dynamics between humans and robots to design systems that are intuitive, safe, and effective. This research is crucial for advancing robotics in areas like healthcare, industry, and daily life, ensuring robots enhance human capabilities while fostering trust and collaboration.

SNLP: Bias, Fairness, Transparency & Privacy

This group explores how to mitigate biases, ensure fairness, enhance transparency, and safeguard privacy in AI-driven language technologies. This work is vital to building ethical, trustworthy, and inclusive AI systems that positively impact society.

Energy-efficient AI

Climate change and its consequenes are one of the most pressing topics of todays society so it is only natural that AI algorithms should play a vital role in the efficient use of our energy.

ML: Auto ML and Hyperparameter Tuning

Automated Machine Learning can save valuable time and energy when using ML algorithms. By continously adjusting hyperparameters, performance can significantly increase.