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, 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 understand better 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.


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,


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 (Toal 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.


 

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

Machine Learning Applications for Operation and Management of Smart Distributed Electrical Energy Network

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

Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications

First job after graduation: Associate Professor, University of Agder


Darshana Abeyrathna, Graduated in 2022

Principal Supervisor: Ole-Christoffer Granmo, Second Supervisor: Morten Goodwin

Novel Tsetlin Machine Mechanisms for Logic-based Regression and Classification with Support for Continuous Input, Clause Weighting, Confidence Assessment, Deterministic Learning, and Convolution

First job after graduation: Senior Researcher DNV


Jivitesh Sharma, Graduated in 2020

Principal Supervisor: Ole-Christoffer Granmo, Second Supervisor: Morten Goodwin

Advances in Deep Learning Towards FireEmergency Application: NovelArchitectures, Techniques and Applications of Neural Networks

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)

Publications


An almost complete list of papers can be found at my Google Scholar.