Projects
KartAI: Smarter Property Data with Artificial Intelligence
Public sector innovation project, 2021–2027
Norwegian Research Council funding: 7 MNOK (Total budget: 15 MNOK)
Partners: Kristiansand Municipality (project owner), Norkart, Norwegian Mapping Authority, University of Agder
KartAI aims to automate and improve municipal case processing related to property registration and building permits using artificial intelligence and advanced data-driven methods. By combining AI with proactive citizen dialogue, the project will enhance efficiency, reduce manual work, and improve decision-making. With standardized data protocols and cutting-edge multimodal machine learning models, KartAI is designed for national scalability, building on the success of the earlier regional initiative KartAI 1.0. The project addresses a shared challenge across all Norwegian municipalities and seeks to set a national standard for AI-assisted public services.
Ny prosess for kunnskapsbasert vurdering og rådgiving ved bruk av kunstig intelligens ved Folkehelseinstituttet
Public sector innovation project, 2024–2027
Norwegian Research Council project number: 349547
External funding: 6.6 MNOK (Total 12 MNOK)
Partners: Norwegian Institute of Public Health (FHI), UiA, UiB
FHI, in collaboration with the University of Agder, SNF, and Kristiansand Municipality, is developing a new AI-powered process for faster and more efficient evidence-based health assessments and advice. The goal is to meet increasing demands from the health sector and general public by automating key steps in the assessment workflow. The project combines expertise in public health, artificial intelligence, and innovation management, and aims to support better decision-making both in daily operations and during crises. The innovation will also serve as a model for broader public sector transformation.
Auto3DMap: Automated 3D Mapping from Aerial Images Using AI
Public sector PhD project, 2022–2026
Norwegian Research Council project number: 338454
External funding: 2.3 MNOK (Total 4MNOK)
Lead institution: Norwegian Mapping Authority (Statens kartverk)
This project aims to automate the creation of 3D building models for national map databases using artificial intelligence and aerial imagery. By replacing manual processes with AI-driven techniques, the project will streamline the generation of building objects for the national FKB-Bygning database. Using overlapping vertical and oblique aerial images, combined with advanced deep learning and photogrammetry, the goal is to accurately reconstruct and geolocate 3D structures. The work will use real-world data from across Norway and contribute to a more efficient and scalable mapping infrastructure.
NAIC: Norwegian Artificial Intelligence Cloud
National research infrastructure project, 2023–2029
Norwegian Research Council project number: 322336
External funding: 49 MNOK
Lead institution: University of Oslo, UiA partner
NAIC aims to build Norway’s most powerful national infrastructure for artificial intelligence. The project will provide computing power, storage, and expert support to researchers across the country. It will be developed in three phases: mapping and optimizing current resources, implementing new technology and training, and finally establishing a central high-performance AI infrastructure. The platform will offer training, helpdesk services, and federated access to partner institutions. After 2029, the infrastructure will transition to national operations under Sigma2.
AUTOCASE: Autonom Saksbehandling med Maskinlæring
NæringsPhD
Norwegian Research Council project number: 346312
Partners: Norkart, UiA
AUTOCASE aims to automate municipal case processing—such as building permits and property-related decisions—using machine learning. The project focuses on developing AI models that can handle complex inputs like text, maps, and technical drawings. With over 90,000 building cases processed annually in Norway, automation offers major efficiency gains. A key part of the project is adapting public data for AI use. The project is supported by the Research Council of Norway and carried out as an industrial PhD.
AIM: AI-drevet Multiformatunivers
Innovation project in the industrial sector, 2022-2026
Norwegian Research Council project number: 332274
External funding: 13 MNOK (Total budget 30 MNOK)
Partners: Dyreparken, InFuture, Qvisten, University of Agder
Dyreparken, Qvisten Animation, inFuture, and the University of Agder are partnering on a 3-year research project called AIM, which aims to use artificial intelligence and machine learning to better understand the audience and the interaction between different formats in creating multiformat universes (large stories that unfold across different formats and evolve over time). The project is supported by The Research Council of Norway and will be collecting, combining, and analyzing data across new sources not currently used or combined by separate industries.
AI4LocalNews: Leveraging AI for Local Democracy
Industrial PhD project, 2024–2027
Norwegian Research Council project number: 354195
Partners: iTromsø, University of Agder, OsloMet
AI4LocalNews aims to develop AI tools that support local journalism by automating data analysis and enhancing newsroom efficiency. The project focuses on helping journalists uncover and communicate important local issues, while maintaining human oversight and ethical standards. Led by iTromsø, the project is carried out as an industrial PhD with support from the Research Council of Norway.
CreateView – Insight to more than what you see
Role: Workpackage leader, technical leader, and main research partner
Norwegian Research Council project number: 325862
Main funding partner: Norwegian Research Council. User-driven research-based
Innovation (BIA)
Project partners: Veterinærinstituttet, Havforskningsinstituttet, University of Agder.
Create Viewaims to develop automated tools for monitoring the health and welfare of farmed fish. The core innovation is a dead fish image scanner (CView LiftUp) and live fish monitoring (CView Eye), combined with diagnostic, production, and environmental data in a large-scale database. Using artificial intelligence and machine learning, the system analyzes patterns that are difficult for humans to detect in time. The result is faster, data-driven decision support that helps prevent fish health issues, reduces losses, improves fish welfare, and increases profitability in aquaculture.
Computer vision to expand monitoring and accelerate assessment of coastal fish
MARINFORSK project, 2021-2025
Norwegian Research Council project number: 325862
External funding 12.2 MNOK (total budget: 13.6 MNOK)
Partners: Havforskningsinstituttet, University of Agder, University of California Santa Cruz, University of Trento, Virginia Polytechnic Institute and State University
CoastVision is a project that aims to use artificial intelligence to improve the identification and re-identification of fish in their natural habitat. They will focus on Atlantic cod, ballan wrasse and corkwing wrasse and will be the final step in a fully automated video analysis pipeline that will identify, track, size and count fish in video feeds from long-term monitoring stations.
Your green, smart and endless wardrobe
Innovation project in the industrial sector, 2020-2024
Norwegian Research Council project number: 309977
External funding: 23.6 MNOK
Project partners: Fjong, BI, NMBU and UIA
FJONG is a company that aims to revolutionize the way we consume clothes by making rentals more attractive than buying new ones. They are developing a unique rental platform that acts as an AirBnB for clothes, where customers can both rent outfits and lend out their own for a cut of the rental price. This project, with UiA, NBMU and BI, expand and conduct further research to overcome key challenges such as behavioral change, sustainability and technical challenges. Behavioral change challenges include how to change consumer habits, sustainability challenges include optimizing the business model and minimizing environmental footprints, and technical challenges include utilizing artificial intelligence and machine learning to create a superior user experience and building a sharing platform that meets the requirements of the value propositions designed and the complex logistics of a peer-to-peer sharing platform. Key to the development is new artificial intelligence techniques for recommendation systems.
Kornmo – production optimization, quality control and sustainability through
the grain value chain
Role: Workpackage leader and main research partner
Main funding partner: Norwegian Research Council. User-driven Research based
Innovation (BIA)
Project partners: Felleskjøpet Agri SA, inFuture AS, NHO Mat og Drikke
The Kornmo-project will develop models to optimize volume, quality, and sustainability
in grain production and track the grain "from soil to table". The project will develop
machine learning approaches for the entire value chain from each shift, via grain
reception and further plant structure and end products.
Deep learning for sleep, mental, and emotional stage recognition from non-contact sensors of mental health patients.
Industrial PhD project, 2020–2024
Focus: Health technology, deep learning, and human activity recognition
This project developed advanced deep learning methods for monitoring human sleep and activity using non-invasive sensors. The research focused on two key areas: automatic sleep stage classification and human activity recognition (HAR), aiming to improve healthcare diagnostics and personalized monitoring.
Using EEG/EOG signals, models like TDConvNet and SleepXAI achieved near-expert accuracy in classifying sleep stages, with an emphasis on explainability for clinical use. In HAR, the project introduced privacy-preserving, interpretable video-based frameworks for fall detection and activity tracking—vital for elderly care and rehabilitation. The results contribute to more transparent, effective AI tools for healthcare, with potential for real-world integration in clinical and home settings.
Wonderful new world? How does the use of artificial intelligence in the customer relationship affect Sparebanken Sør's ability to act as an ethical,
socially responsible relationship player?
Role: Main research partner
Main funding partner: Norwegian Research Council. Industrial Ph.D.
Project partners: Sparebank Sør, University of Agder
The project aims to provide increased knowledge about how the use of artificial
intelligence can affect the bank's ability to act as an ethical, socially responsible
relationship player. The ambition is to provide insight that will strengthen the bank's
ability to make the right decisions in its work with digitization, automation, and the use
of artificial intelligence.
Predictive maintenance of drilling equipment using deep neural networks
Role: Main research partner
Main funding partner: Norwegian Research Council. Industrial Ph.D.
Project partners: MhWirth, University of Agder
This project explores the potential for using deep learning to predict drilling
equipment problems and estimate the remaining useful life. The project will specifically
address this problem by using real-time data from 11 rigs in operation. The focus will
be to develop and apply deep neural networks to enable autonomous decision-making
for improving service performance through condition-monitoring, predictive
maintenance, and spares management.
Deep Neural Networks for Energy Efficiency in High-Tech Warehouses
Role: Main research partner
Main funding partner: Norwegian Research Council. Industrial Ph.D.
Project partners: Login Eiendom, Rema 1000, University of Agder
The goal of this project is to promote the latest innovations in artificial intelligence (AI)
for energy-related operations in warehouses. More specifically, the output of this
research will be to automatize the energy-related management in a technologically
advanced warehouse to reduce the operational cost and to improve the energy
efficiency based on various data inputs and the-state-of-the-art deep neural network.
Graduate students
Ph.D. Students:
Li Meng, Graduated in 2025
State representation in Reinforcement Learning
Supervisors: Paal Engelstad, Morten Goodwin Anis Yazidi
First job after graduation: PostDoc at OsloMet
Rupsa Saha, Graduated in 2025
Principal Supervisor: Ole-Christoffer Granmo ,Second Superviros: Morten Goodwin
Towards a Relational Tsetlin Machine in Natural Language Processing
First job after graduation: Postdoc UiA
Micheal Dutt, Graduated in 2024
Principal Supervisor: Morten Goodwin Second Superviros: Christian Omlin
Explainable Deep Learning for Human Behaviour Understanding: Sleep Monitoring, Human Activity Recognition, and Future Opportunities for Healthcare
First job after graduation: Consultant Egde
Sven Opalic, Graduated in 2023
Principal Supervisor: Mohan Lal Kolhe, Co-supervisor: Morten Goodwin. Lei Jiao
Advanced Warehouse Energy Storage System Control Using Deep Supervised and Reinforcement Learning
First job after graduation: Fagsjef energiteknikk og innovasjon, Ph.D. i AI til energistyring, RELOG
Rohan Kumar Yadav, Graduated in 2022
Principal Supervisor: Lei Jiao, Second Supervisor: Morten Goodwin
Interpretable Architectures and Algorithms for Natural Language Processing
First job after graduation: Senior NLP Data Scientist at Kobler AS
Nils Jakob Johannesen,Graduated in 2022
Principal Supervisor: Mohan Lal Kolhe, Second Supervisor: Morten Goodwin
First job after graduation: Associate Professor, University of Southern Norway
Per-Arne Andersen, Graduated in 2022
Principal Supervisor: Morten Goodwin, Second Supervisor: Ole-Christoffer Granmo
First job after graduation: Associate Professor, University of Agder
Darshana Abeyrathna, Graduated in 2022
Principal Supervisor: Ole-Christoffer Granmo, Second Supervisor: Morten Goodwin
First job after graduation: Senior Researcher DNV
Jivitesh Sharma, Graduated in 2020
Principal Supervisor: Ole-Christoffer Granmo, Second Supervisor: Morten Goodwin
First job after graduation: PostDoc, University of Agder
Mehdi Ben Lazreg, Graduated in 2020
Principal Supervisor: Morten Goodwin, Second Supervisor: Ole-Christoffer Granmo
A Neural Network-Based Situational Awareness Approach for Emergency Response
First job after graduation: Machine Learning Developer Infront ASA
Ongoing:
Karl Audun Kagnes Borgersen, Clothes Rental Recommendation and Onboarding with Deep Learning
Daniel Biermann, Deep Learning for Personalized Chatbot-based Education
Jakob Michael Voigt, Enabling Personalized Education through the use of Machine Learning and Learning Analytics
Martin Holen, A reinforcement learning-based approach for autonomous vehicles in simulated and real-world environments.
Sander Jyhne, Deep learning for map segmentation
Morten Grundetjern, Synthetic personas
Henrik Brådland, Autonom saksbehandling med maskinlæring
PostDocs:
Vimala Nunavath, 2019 - 2021
Deep learning for health analysis
First job after completion: Associate Professor University of Southern Norway
Aditya Gupta, 2021 -
A view of more than you see of aquaculture.
Rashmi Gupta, 2021-2023
Production optimization, quality management, and sustainability through the grain value chain
Firs tjob after completion: Associate Professor Kristiania University College Norway
Jeppe Have Rasmussen, 2020 - 2023
Deep learning for the biomechanics of sound production in cod.
Graduated Master Students:
(Will come)