Projects
Norwegian AI Cloud
NFR prosjekter:322336
AIM: AI-drevet Multiformatunivers
Innovation project in the industrial sector, 2022-2025
Norwegian Research Council project number: 332274
External funding: 13 MNOK (Total budget 30 MNOK)
Partners: Dyreparken, InFuture, Qvisten, University of Agder
Dyreparken and Qvisten Animation AS join forces with inFuture AS and The University of Agder on an ambitious research project in artificial intelligence.
The project AIM – AI-driven Multiformat universe – will research how best to convey new stories across films, series, games and park activities, also known as multiformat universes. Multiformat universes are large stories that unfold across different formats and evolve over time. (Marvel Universe and DC Universe are examples of large multiformat universes.) AIM is supported by The Research Council of Norway through the IPN-program (Innovation Project for the Industrial Sector), will run for 3 years and has a total project of 30 mnok.
The aim of AIM is to engage the audience more actively than today when new stories are created. The project will utilize artificial intelligence and machine learning to better understand the audience and the interaction between different formats. This is currently only done by the largest global players such as Netflix and Disney. The difference is that the streaming companies have access to vast amounts of data that are unavailable to others. The project must therefore collect, combine, and analyze data across new sources. These sources are currently not combined, and to the extent they are used it is done by separate industries.
In addition to Dyreparken and Qvisten Animation the project consists of data scientists from inFuture AS, researchers from the Center for Artificial Intelligence Research (CAIR) and The Department of Management at The University of Agder (UiA). The project will also cooperate with The Cultiva Foundation and Visit Sørlandet AS to ensure knowledge transfer with culture and travel companies in Agder.
CreateView – Insight to more than what you see
Role: Workpackage leader, technical leader, and main research partner
Main funding partner: Norwegian Research Council. User-driven Research based
Innovation (BIA)
Project partners: Veterinærinstituttet, Havforskningsinstituttet, University of Agder,
CoastVision
NFR prosjektnummer: 325862
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
Imagine having access to a never-ending shared wardrobe. And also making money off the clothes you own but only seldom wear. The textile industry is among the most polluting and resource-demanding, emitting more greenhouse gases than shipping and air traffic combined. Since 2000, global clothing production has doubled, but utilization has dropped. Every third garment is never used.FJONG aims to revolutionize the way we consume clothes by making rentals more attractive than buying new ones. We develop a unique rental platform, a convenient and environmentally friendly AirBnB for clothes. Our customers are able to both rent outfits and lend out their own for a cut of the rental price. 60.000 people have already shown interest in our service and a large share of our inventory is sourced through private lenders.We are now ready to expand, both in Norway and internationally. However, further research is required to overcome some key challenges:- Behavioral change challenge: How do we change consumer habits? Which factors make people shift from buying to renting, or from throwing away to lending out? What is important for retailers to lend out?- Sustainability challenge: How can we optimize our business model from an environmental point of view, while still being scalable and financially viable? How can we minimize footprints from our own value chain, while at the same maximize our impact for sustainable clothing consumption?- Technical challenge: Can artificial intelligence and machine learning help create a superior user experience that makes renting more attractive than buying? How can we build a sharing platform that meets the requirements of the value propositions designed and the complex logistics of a peer-to-peer sharing platform?
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.
A machine learning approach for non-intrusive physiological and behavioural monitoring in mental healthcare settings
Role: Main research partner
Main funding partner: Norwegian Research Council. Industrial Ph.D.
Project partners: Egde, University of Agder
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:
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.
Micheal Dutt, A machine learning approach for non-intrusive physiological and behavioral monitoring in mental healthcare settings.
Sander Jyhne, Deep learning for map segmentation
Sven Opalic, Deep Neural Networks for Energy Efficiency in High Tech Warehouses
Vera Barstad, Deep Neural Networks for Efficient Analysis of Magnetic Resonance Imaging
Vera Szabo, Predictive maintenance of drilling equipment using deep neural networks
Li Meng, Integrating novel self-supervised learning techniques into deep reinforcement Learning: learning behaviors from few expert examples by GANs and semi-supervised learning
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.-
Deep learning for the biomechanics of sound production in cod.
Graduated Master Students:
(Will come)