Research

Responsible use of Artificial Intelligence (AI) can make a positive contribution to our society. Therefore, we develop AI to empower people and support growth of individual capabilities. We bring fundamental research into practice - our research is driven by real-world problems and should make a difference. We openly engage with the general public and foster discussions about the impact of AI in society. Furthermore, we follow a student-centric teaching philosophy that aims to encourage curiosity.

Research Directions:

	Deep Learning Techniques: We take a multi-disciplinary perspective to develop inductive biases to increase generalisation capabilities of deep neural networks. Particularly, we invent methods which induce and exploit structure of the data, the model, and its training.
	Explainable AI: We develop novel techniques for explaining how self-learning AI systems (like Deep Neural Networks) perform their tasks. We particularly focus on providing explanations that are understandable for non-experts while still being faithful. We use established methodology and models from neuroscience and adapt them to build and understand AI systems.
	Audio-Processing: Speech, Music Information Retrieval: We integrate concepts from deep learning, digital signal processing and auditory perception to design new methods for audio classification, recommendation and generation.
	Medical Imaging: We rethink medical image processing algorithms to improve diagnosis of patients. We focus especially on the refinement of x-ray image styles and their quality.
	Brain-Computer Interface (BCI) systems: We design BCI systems to support people in their daily activities and enhance their quality of life. We aim to design more efficient systems by leveraging AI at every step of BCI systems: Transforming brain activities into a meaningful representation, classifying and turning them into actions.
	Education: We research on how technology can enhance the learning experience and rethink the way we educate our students about AI.