Prof. Dr. Rolf Drechsler
Director at DFKI, Head of the Cyber-Physical Systems (CPS) research department, University of Bremen/DFKI
Dr. N. Benjamin Erichson
Senior Research Scientist – International Computer Science Institute (Universität von Kalifornien in Berkeley, USA)
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.
ML: Transparent, Interpretable, Explainable ML
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.
ML: (Inverse) Reinforcement Learning
Reinforcement Learning is a main framework in the field of machine learning. The group focuses on increasing the efficiency of algorithm development by combining reinforcement learning with the power of automated machine learning.
ROB: Behavior Learning & Control
Robots are being used more and more in our everyday lives. It is important that we investigate how we can best integrate and train them in order to achieve a positive relationship between humans and robots.
CV: Approach to Computer Vision
