The "Scientific Centre for Methods of Artificial Intelligence and Data Science" (WiZARD) was founded by three Schools. The aim is to develop methods in a systematic and interdisciplinary manner in order to build an efficient bridge between theoretical basic research in data science and practical utilisation in data-intensive scientific disciplines.
It is a growing field: analysing large amounts of data has become an essential part of research in almost all scientific disciplines. But which methods are particularly effective for this and how can they be developed further? The "Scientific Centre for Methods of Artificial Intelligence and Data Science", or "WiZArD" for short, which was founded at the University of Oldenburg in January, is looking into this. The aim is to systematically pool new approaches to mathematical and algorithmic methods, develop them further and apply them in various disciplines.
"We want to find out how the potential of artificial intelligence (AI), machine learning and data science can be used in a targeted manner - initially in the fields of medicine, natural and life sciences, economics, law and Computing Science," says Centre Director Nils Strodthoff, who holds the professorship for "eHealth: Interpretable and Explainable Learning Algorithms" at the Department of Health Services Research. As a scientific centre, WiZArD will in future play a coordinating role in the tasks of the participating Institutes and Schools related to its own focus.
WiZArD: Basic and application-orientated
In interdisciplinary teams, the researchers want to network more closely and focus on basic and application-orientated methods for large amounts of data. According to Strodthoff, this is a key to applications in numerous research areas at the university. Another task of the new centre is to advise individual Schools and the Presidential Board on the technical infrastructure in data science and AI. The researchers are also planning to incorporate current research findings from the field of data science into teaching.
WiZArD is dedicated to both data-centred and model-based aspects of artificial intelligence. On the data side, the focus is on methodological questions of data infrastructure and semantic interoperability. On the modelling side, the initiative covers model architectures and quality criteria such as explainability and uncertainty quantification. By combining both dimensions, WiZArD will drive forward practical applications in the departments of the three participating Schools.
"New members from all School departments with a connection to data science, AI and machine learning are always welcome and can actively contribute to the centre," says Strodthoff. Interested parties can send enquiries by email to .
