Artificial intelligence and machine learning

Next Generation AI represents DIREC’s current strategic initiative for shaping the future of artificial intelligence. Through new collaborative research projects across academia and industry, these activities focus on developing advanced AI technologies that deliver real-world value for society, businesses, and the public sector.

Alongside this initiative, DIREC has built a strong portfolio of projects within Artificial Intelligence and Machine Learning, covering areas such as machine learning methods, computer vision, natural language processing, embedded AI, and privacy-preserving technologies.

Next generation AI

DIREC has granted DKK 27 million to seven new research projects focused on developing the next generation of AI. The aim is to secure Danish digital breakthroughs that deliver tangible benefits to society. This initiative is part of the research fund allocation granted to DIREC in November 2024 by the Danish Ministry of Higher Education and Science.

REINS: Adaptive AI for Industry – Without the Cloud

The REINS project unites B&O, Leica Geosystems, the Alexandra Institute, DTU, and SDU in a shared mission: to develop AI that runs efficiently on devices that operate in dynamic environments with very limited computing power. The result is technology that saves energy, responds instantly, and can operate anywhere.

Read More »

GREENSQL: Green digitalization starts in the database

Databases track information, move it back and forth, and ensure seamless integration across systems — but all of this consumes energy. A lot of energy. What if it could be done more efficiently? This question is central to this project. By optimizing code and databases with AI, GREENSQL has the potential to drastically reduce energy consumption.

Read More »

1813AI: Responsible AI for the Emergency Hotline

1813AI focuses on citizens with injuries and is developing a citizen-facing, adaptive AI chat solution that, through a new self-service app, will provide guidance and retrieve information during wait time. This creates faster access to help, reduces staff stress, and contributes to a more equitable and efficient healthcare service.

Read More »

MOTUS: Safe and Responsible Co-bots in Healthcare and Industry

Many researchers are working on bringing advanced AI capabilities to co-bots. However, AI is often unaware of its own mistakes. When the AI is simply ChatGPT writing an e-mail, at worst, its mistake will mean that e-mail makes no sense. But if the AI controls a co-bot, its malfunction can injure workers or destroy the robot’s surroundings. 

Read More »

FAIRFM: Towards equal access to ultrasound scans

The quality of a fetal scan depends on the operator’s experience and the mother’s individual physiology, meaning some women receive more accurate assessments than others. The FairFM project aims to eliminate this disparity by developing AI that detects and corrects biases, ensuring all pregnant women have equal access to early and accurate fetal diagnostics.

Read More »

AI project portfolio

Below you can find a portfolio of projects within Artificial Intelligence and Machine Learning, covering areas such as machine learning methods, computer vision, natural language processing, embedded AI, and privacy-preserving technologies.

While many of these projects have now been completed, they continue to represent important contributions to AI research and application development. Together, these efforts reflect the breadth of DIREC’s engagement in AI — from established research domains to new, forward-looking initiatives within Next Generation AI.

Insights / NEWS

Research area manager

Mads Nielsen

Professor

University of Copenhagen
Department of Computer Science
E: madsn@di.ku.dk
T: +45 24 60 05 99​

University project leads

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

Publications

Towards Autonomous Multi-UAV U-Space Operation Planning
Kaspar A.R. Grøntved, Jes Hundvadt Jepsen, Anders Lyhne Christensen, Kjeld Jensen, Ulrik Pagh Schultz Lundquist, Miguel Campusano

Fostering Trust Through User Interface Design in Multi-Drone Search and Rescue
Johanna Ahlskog, Maria-Theresa Bahodi, Artur Lugmayr, Timothy Robert Merritt

Show Me What’s Wrong: Impact of Explicit Alerts on Novice Supervisors of a Multi-Robot Monitoring System
Maria-Theresa Bahodi, Niels van Berkel, Mikael B. Skov & Timothy Robert Merritt

AdaBoost is not an Optimal Weak to Strong Learner
Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert

An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery
CK Wong, M Lin, A Raheli, Z Bashir, MBS Svendsen, MG Tolsgaard, A Feragen, A Nymark

Challenges and Requirements in Multi-Drone Interfaces
Hoang, MT.O, van Berkel, N., Skov, M.B., and ., Merritt, T.

Drone Swarms to Support Search and Rescue Operations: Opportunities and Challenges
Hoang, MT.O., Grøntved, K.A.R., van Berkel, N., Skov, M.B., Christensen, A.L., Merritt, T

I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks
M Lin, A Feragen, Z Bashir, MG Tolsgaard, AN Christensen

Learning Topological Similarity for Curvilinear Structure Segmentation
M Lin, K Zepf, AN Christensen, Z Bashir, MBS Svendsen, M Tolsgaard, A Feragen

Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction
P Pegios, EEP Sejer, M Lin, Z Bashir, MBS Svendsen, M Nielsen, E Petersen, A Nymark, M Tolsgaard, A Feragen

On convex conceptual regions in deep network representations
Tětková, L., Brüsch, T., Scheidt, T.K., Mager, F.M., Aagaard, R.Ø., Foldager, J., Alstrøm, T.S. and Hansen, L.K.

MM Algorithms to Estimate Parameters in Continuous-time Markov Chains
G Bacci, A Ingólfsdóttir, KG Larsen, R Reynouard

Removing confounding information from fetal ultrasound images
K Mikolaj, M Lin, Z Bashir, MBS Svendsen, M Tolsgaard, A Nymark, A Feragen

Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
Andreasen LA, Feragen A, Christensen AN, Thybo JK, Svendsen MBS, Lekadir K, Tolsgaard MG

Unionized Data Governance in Virtual Power Plants: Poster
Niels Ørbæk Chemnitz, Phillippe Bonnet, Sebastian Büttric, Irina Shklovski, Laura Watts

Using Signals to Support Trust Building in Clinical Human-AI Collaboration
Naja Kathrine Kollerup, Mikael B. Skov, Niels Van Berkel

Challenges arising in a Multi-Drone System for Search and Rescue
Maria-Theresa Oanh Hoang, Niels van Berkel, Mikael B. Skov, Timothy Merritt