ML: Multimodal Learning
As the world around us is complex, it is important to incorporate multiple modalities into systems created to support humans whether it is in medicine as robotic surgical assistants or other work environments.
PEAI: Societal Impact of AI
Societal impact of AI is one of the most talked about topics in media at the moment due to its grave importance. Focusing on human-centered needs whether in applications in healthcare or governance of sociotechnological systems is essential.
ML: Time-Series / Data Streams
Time series analysis occurs in many different areas, from medical data (e.g. EEG) to mobility and transportation. This micro focus group concentrates on CNN models to analyze their data.
DMKM: Linked Open Data, Knowledge Graphs & KB Completion
The organization of knowledge is fundamental to the use of AI algorithms, as it often facilitates explainability and transparency.
Cyber/ IT-Security
Software systems must be secure and robust against external attacks. This group focuses on cyber and IT security.
ML: Graph-based Machine Learning
Graph-based machine learning uses coherent data structures (graphs) to model relationships and dependencies. This approach improves the ability of AI to analyze and predict complex relationships in various domains, from social networks to recommendation systems.
CV: Vision for Robotics & Autonomous Driving
Image recognition and processing are not only crucial for the future of autonomous driving, but also in robotics. LiDAR sensors and deep neural network architectures help to accurately process different types of perceptions.
CV: Medical and Biological Imaging
The use of computer vision methods in medicine is increasing rapidly. The generation of reliable and accurate images of small tissue types holds enormous potential in the healthcare sector and promises great benefits for patients.
Explainable AI
The need for understandable AI is increasing in line with the growing number of algorithms that influence our daily lives. By improving the interpretation of predictions made by models, we can understand their behavior and optimize their performance.
SNLP: Conversational AI/Dialogue Systems
In the era of ChatGPT, the study of large language models has become of great importance and the possibilities of use are very diversified. Therefore, it is particularly important to create stable models.
