AI - Machine Learning, Computer Vision, Natural Language Processing

AI is seen as one of the technologies with most impact on society in the coming years with a potential to boost the Danish GDP with 1.6% annually. Denmark has a unique collection of public and registry data. And as one of the most digitised countries in the world, more digital data are generated every day.


The objectives are to increase the capacity and theoretical knowledge on AI as well as the engineering skills, and to show impact on real world applications working together with start-ups, industry and public institutions. A number of demonstration projects will show the potential and impact.

Obvious synergies with all other disciplines lie in the potential use of machine learning where rule-based decisions have been used earlier. Big data analysis relies on an increasing degree on machine learning, and image and text data make part of big data corpora. Likewise new ML algorithms and proof of their performance can find input from for example the Efficient Algorithms and Data Structures WS4.

Human computer interfaces and information visualisation exploit machine learning and insight into AI systems relies on the development on advanced information visualisation. Likewise, cyber physical systems, autonomous systems use machine learning and IoT make sensor systems for AI. Verification and cybersecurity rely on AI to an increasing degree. Dually, it is also promising with an emerging focus on robustness verification of deep neural networks.


SciTech project

Privacy and Machine Learning

There is an unmet need for decentralised privacy-preserving machine learning. Cloud computing has great potential, however, there is a lack of trust in the service providers and there is a risk of data breaches. A lot of data are private and stored locally for good reasons, but combining the information in a global machine learning system could lead to services that benefit all.

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Bridge project

Low-Code Programming of Spatial Contexts for Logistic Tasks in Mobile Robotics

Logistics tasks in low-volume production represent an important opportunity for automatization via mobile robots. However, for successful application easy programming solutions are needed that address the variability of the spatial context in full 3D. The project partners SDU and RUC aim to provide an adaptive and re-configurable low-code programming approach to this problem and demonstrate the approach with the mobile platform of Enabled Robotics.

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Bridge project

Multimodal Data Processing of Earth Observation Data

The Danish partnership for digitalization has concluded that there is a need to support the digital acceleration of the green transition. This includes strengthening efforts to establish a stronger data foundation for environmental data. Based on observations of the Earth a range of Danish public organizations build and maintain important data foundations. Such foundations are used for decision making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark.

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Workstream manager

Mads Nielsen


University of Copenhagen
Department of Computer Science
T: +45 24 60 05 99​

Contributing researchers

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Aalborg University
Department of Computer Science

Technical University of Denmark
DTU Compute

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

Roskilde University
Department of People and Technology