Alexander Selvikvåg Lundervold

Field of work

How can we transform artificial intelligence methods into practical, AI-based solutions? What role can artificial intelligence play in sectors such as education, medicine, and healthcare, both now and in the future?

Alexander is a professor of artificial intelligence (AI) at Western Norway University of Applied Sciences (HVL) and an engaged communicator of AI and its practical applications. His work lies at the intersection of machine learning, artificial intelligence, and software engineering, with a particular focus on development, evaluation, and implementation of AI-based solutions in medicine, healthcare, and education.

At HVL, he leads work on AI-based innovation and efficiency improvement through roles in the AI coordination group, the Artificial Intelligence Engineering research group, and HVL's AI lab. He teaches artificial intelligence to educators and administrative staff, and regularly advises organizations on how AI can improve services and create new opportunities.

Since 2018, he has led the Medical AI activities at the Mohn Medical Imaging and Visualization Centre (MMIV) at Haukeland University Hospital, focusing on developing machine learning-driven software for medical diagnosis and treatment.

Public Speaking

Alexander has given over 100 presentations for the healthcare sector, academia, business, and public organizations. The presentations span four main areas: 

Health and medicine: Medical AI and future healthcare services for hospitals, health enterprises, and medical conferences.

Education and academia: AI in teaching, research innovation, and digital learning tools for universities, colleges, and high schools.

Business and innovation: Practical applications of AI for efficiency and business development, in large corporations and local business networks.

Society and ethics: Public lectures and panel debates about AI's role in societal development, democracy, and ethical perspectives.

Projects

• ASIS - AI-supported services for image-diagnostics in Western Norway, funded by the Western Norway Regional Health Authority (2025-2029)
• AIMS Norway – Artificial Intelligence in Mammography Screening in Norway, funded by the Western Norway Regional Health Authority (2023–2025).
• Part of the project leadership and PI in a machine learning work package in WIML: Workflow-integrated machine learning at the MMIV, funded by the Norwegian Research Council (2020–2024).
• PI of a work package in the project AI-Support in Medical Emergency Calls: The AISMEC-project, funded by the Norwegian Research Council (2022–2025), led by Guttorm Brattebø from Helse Bergen HF and KoKom.
• Partner in AkademiX, 2023-
• Part of the coordinating team of the Norwegian research network PRESIMAL: Precision imaging and machine learning for better patient care, funded by Nasjonal samarbeidsgruppe for helseforskning i spesialisthelsetjenesten (2021-2023), with partners from all the health regions in Norway and their universities.
• Co-PI in a machine learning work package in the Digital Life Norway project Towards better computational approaches and responsible innovation strategies in early drug discovery – application to antibiotics and COPD (2019–2023), led by Nathalie Reuter from UiB.
• Co-PI of the project Computational medical imaging and machine learning – methods, infrastructure and applications, funded by the Trond Mohn Foundation (2018–2022).
• Co-PI in a work package of the project Imaging biomarkers for precision medicine in Acute Myeloid Leukemia (AML), led by Cecilie Brekke Rygh from HVL, funded by the Western Norway Regional Health Authority (2020–2022). The main objective of the project is to evaluate the role of PET and PET-derived predictive imaging biomarkers in assessing early treatment response in AML patients to improve overall outcomes.
• Member of the project Precision imaging in gynecologic cancer at MMIV, led by prof. dr. med. Ingfrid Haldorsen. The aim of the project is to integrate imaging biomarkers into clinically relevant treatment algorithms for gynecologic cancers.
• Member of the project Disrupt, potentiate and rewire — a novel framework for understanding electroconvulsive therapy at MMIV, led by Leif Oltedal , financed by the Western Norway Regional Health Authority.
• Member of the project Deep learning in image diagnostics: transfer learning and active learning for efficient use of data and radiological expertise at MMIV, led by Sathiesh Kaliyugarasan, financed by the Western Norway Regional Health Authority (2020-2023).
• Member of the project From cognitive aging to dementia — a longitudinal imaging-based machine learning approach, led by Alexandra Vik, financed by the Western Norway Regional Health Authority (2020-2023).
• Part of Kunstig intelligens i norsk helsetjeneste (KIN), a national network for artificial intelligence in health care. I was part of the coordinating team of the network in the period 2020—2022.
• Head of the project group in an AI committee established by Helse Vest RHF. The goal is to investigate machine learning based software solutions for imaging diagnostic support that could potentially be useful in the established radiological workflow in Helse Vest.
• Member of a committee established by the Faculty of Medicine, UiB. Our report (Aug. 2020) proposed a plan for establishing Medical AI as a cross-institutional and cross-disciplinary field of research, innovation and education in Bergen.

Supervision

PhD

• Sathiesh Kaliyugarasan: Deep learning in image diagnostics: transfer learning and active learning for efficient use of data and radiological expertise. Funded by the Western Norway Regional Health Authority (2020–2023). He defended his thesis October 3^rd, 2023
• Samaneh Abolpour Mofrad (2018–2021): Learning and Cognition in Brain and Machine: Prediction of dementia from longitudinal data and modelling memory networks. She defended her thesis November 26, 2021.

Co-supervision

Ongoing

• Kasia Kazimierczak, New strategies for analysis of resting state fMRI, together with Karsten Specht (main supervisor) and Vince Calhoun.
• Emil Kristoffer Iversen, Artificial intelligence support in stroke calls: The AISI-study, together with Guttorm Brattebø (main supervisor), Anette Fromm and Hege Ihle-Hansen.

Completed

• Muhammad Ammar Malik, Unsupervised and scale-free discovery of genetic factors influencing brain structure and function, together with Tom Michoel (main supervisor) and Inge Jonassen, Department of Informatics, UiB.
   

MSc

• Eilert Skram and Daniel Kristiansen Gunleiksrud (2023–2025). Topic: AI and education: Constructing and evaluating an LLM-based course assistant.
• Øyvind Grutle and Jens Andreas Thuestad (2021–2023). Speech-to-text models to transcribe emergency calls (EMCC / 113)
• Kjetil Dyrland (2020–2022). Evaluation and Improvement of Machine Learning Algorithms in Drug Discovery.
• Jostein Digernes and Carsten Ditlev-Simonsen (2020–2022). A workflow-integrated brain tumor segmentation system based on fastai and MONAI.
• Anders Benjamin Grinde and Bendik Johansen (2019–2021). Using Natural Language Processing with Deep Learning to Explore Clinical Notes.
• Malik Aasen and Fredrik Fidjestøl Mathisen (2019–2021). De-identification of medical images using object-detection models, generative adversarial networks and perceptual loss.
• Adrian Storm-Johannessen and Sondre Fossen-Romsaas (2018–2020). Medical image synthesis using generative adversarial networks.
• Sivert Stavland (2018–2020). Machine learning and electronic health records.
• Sindre Eik de Lange and Stian Heilund (2017–2019). Autonomous mobile robots: Giving a robot the ability to interpret human movement patterns, and output a relevant response.
• Sathiesh Kumar Kaliyugarasan (2017–2019). Deep transfer learning in medical imaging. A study of how to best use transfer learning when training deep neural networks for biomedical image analysis.
• Sean Meling Murray (2017–2018). An Exploratory Analysis of Multi-Class Uncertainty Approximation in Bayesian Convolutional Neural Networks.

BSc

• Preben Andersen and Andrea M. Svendheim (2024). LLMs and fish health. In collaboration with Lerøy Seafood Group
• Harald Giskegjerde Nilsen and Sindre Kjeldrud (2024). LLMs for health advice. In collaboration with the Faculty of Medicine, UiB.
• Bendik Mathias Johansen and Kathinka Neteland (2019). Automating Reports on Water Consumption and Availability. A data science project together with Bouvet and Bergen Vann.
• Jon Einar Haraldsvik, Stian Gudvangen Gjerløw, Didrik Fanuelsen Tranvåg (2015). Tryg Maintenance App – A cross-platform application using Appcelerator Studio Cloud Services and Arrow DB. The students developed a cross-platform mobile application for Tryg Forsikring. The project was awarded "best bachelor project" at the department in 2016. The students went on to start Appivate AS.

Postdocs, main mentor

Completed

• Alexandra Vik: From cognitive aging to dementia – a longitudinal imaging-based machine learning approach. Funded by the Western Norway Regional Health Authority (2020–2022).
• Piero Mana. Worked in the RESPOND3 drug discovery research project. Funded by the Norwegian Research Council (2020–2023).

Courses taught

Artificial intelligence engineering for software engineers. Medical AI for medical and biomedical students. Educational AI for teachers.

Courses

• DAT158: Machine learning engineering. A practical, project-based, hands-on exploration of the fundamentals of machine learning, focusing on applications of machine learning and the core software engineering principles for successful deployment of machine learning models.
• DAT255: Deep learning engineering. MSc course on practical applications of deep neural networks and the construction of deep learning-based software solutions.
• FD28: Artificial intelligence in education
• ADA524: Large language models. A comprehensive introduction to LLMs within the scope of applied computer science and engineering. Foundational theory, practical tools, and methodologies that drive LLMs' current development and application. 
• DAT801: Machine learning for business development
• ELMED219: Artificial intelligence and computational medicine. A collaboration between the Department of Biomedicine, University of Bergen, Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, and Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital. The course is offered to both medical students and engineering students and encourages collaborations between these disciplines.
• HVL-DLN-AI: A hands-on course on artificial intelligence in computational biotechnology and medicine
• PCS956: Recent trends in applied machine learning

Research areas

• Machine learning engineering
• Artificial intelligence
• Medical AI
• Data analysis
• Computational medicine

Research groups

• HVL Machine Learning Engineering
• MMIV-Medical AI
• HVL Data Science Group