Applied Artificial Intelligence

Contact

Group Lead

Prof. Dr.-Ing. Daniel Sonntag

Office

Office hours by appointment

Address

Stiftungsprofessur Künstliche Intelligenz
Marie-Curie Str. 1
D-26129 Oldenburg

See also

Applied Artificial Intelligence

The “Applied AI” research group, which is part of the Interactive Machine Learning research department at the German Research Center for Artificial Intelligence (DFKI), focuses on applying and adapting artificial intelligence methods to, for example, industrial and medical applications. Sustainability is also a major topic in Oldenburg.

Research-relevant application aspects primarily concern the use of learning systems and intelligent user interfaces. Key areas of focus include multimodal input and output and multisensor applications involving environmental and state recognition, sensor data processing, and issues of real-time performance and interactivity when learning from very large or very small datasets, as well as reliability aspects (including trust in AI and explainable AI).

Regardless of specific subject areas, the overarching research goal is to develop guidelines for the practical application of artificial intelligence. In addition, basic research is conducted in the interdisciplinary field of human–machine interaction in combination with machine learning.

Student AI transfer projects are especially important to us. You can find a selection here: iml.dfki.de. For Bachelor's or Master's theses, please contact or .

News

Best PhD Colloquium Paper Award at GoodIT 2024

Hannes Kath, Thiago Gouvêa and Daniel Sonntag were honoured with the Best PhD Colloquium Paper Award for their work "Active and Transfer Learning for Efficient Identification of Species in Multi-Label Bioacoustic Datasets" at the 4th International Conference on Information Technology for Social Good (GoodIT 2024) in Bremen.

The research presents innovative approaches to the detection of known and unknown species in large bioacoustic datasets and investigates methods to precisely identify as many species as possible with minimal annotation effort.

Hannes Kath, Thiago Gouvêa and Daniel Sonntag were honoured with the Best PhD Colloquium Paper Award for their work "Active and Transfer Learning for Efficient Identification of Species in Multi-Label Bioacoustic Datasets" at the 4th International Conference on Information Technology for Social Good (GoodIT 2024) in Bremen.

The research presents innovative approaches to the detection of known and unknown species in large bioacoustic datasets and investigates methods to precisely identify as many species as possible with minimal annotation effort.

(Changed: 11 Feb 2026)  Kurz-URL:Shortlink: https://uol.de/p79699n10261en
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