Alexander Selvikvåg Lundervold
Field of work
How do you go from artificial intelligence methods to artificial intelligence-based solutions? What's the role of artificial intelligence in medicine and healthcare, now and in the future?
Most of my research, teaching, and supervision activities are in the intersection of machine learning, artificial intelligence, and software engineering. I focus on how to develop machine learning-powered software, also known as machine learning engineering. In particular, I'm engaged in the development, evaluation, and implementation of AI-based solutions in medicine and health (Medical AI), and in education.
At HVL, I'm part of the Artificial Intelligence Engineering Group. Since 2018, I've spearheaded the activities in medical AI at the Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, where I'm also part of the center leadership team.
A more detailed deion of my research and links to my publications can be found on my homepage: https://alexander.lundervold.com/
Selected presentations over the past five years
See also https://alexander.lundervold.com.
- Foredrag og paneldebatt under FHS sin forskningskonferanse, 2023, Profesjoner i spill, 6. desember, 2023
- Keynote under Norwegian ICT Conference for Research and Education 2023, The evolving realm of generative AI, 27.-30. november, 2023
- Foredrag for Radiologisk avdeling, Helse Bergen, Kunstig intelligens – venn eller fiende?, 17. november, 2023
- What every software engineer should know about generative AI, Part 2. HVL, 16. november, 2023
- Foredrag og paneldebatt om KI i utdanning under Rådgiverseminar for vgs. og UH 2023 arrangert av Utdanning i Bergen, Solstrand, 14 november, 2023
- Kunstig intelligens og forskning. Avdeling for forskning, internasjonalisering og innovasjon, HVL, 9. november, 2023.
- AI og radiografer. Radiografiens dag, 8. november, 2023.
- Introduksjon til kunstig intelligens.Yrkesfaglærerløftet, HVL, 7. november, 2023.
- Foredrag under Realfagsdagen 2023, Bergen, 6. november, 2023.
- Foredrag for NITO. Diagnostikk i møte med kunstig intelligens. 25. oktober, 2023.
- Generativ kunstig intelligens: muligheter og utfordringer. Læringslab, 25. oktober, 2023
- Paneldebatt under Sampol-konferansen 2023: (U)begrenset Teknologi- Demokratiets Fremgang eller Undergang?. Tema: Governing AI: Ethics, regulations and technological innovation. Bergen, 18. oktober, 2023.
- What every software engineer should know about generative AI, Part 1. HVL, 4. oktober, 2023
- Innlegg under Stormøtet til Kompetanseforum Vestland. Tema: KI ved HVL. Bergen, 5. oktober 2023.
- Kunstig intelligens for HR. Avdeling for HR, HVL, 28. september, 2023.
- Innlegg under Innføring i universitetspedagogikk ved HVL. Kunstig intelligens i undervisning. Bergen, 21. september, 2023.
- Kunstig intelligens--mirakel eller monster? Forskningsdagene UNG 2023. Bergen, 20. september, 2023.
- Innlegg under Innføring i universitetspedagogikk ved HVL. Artificial intelligence in education. Bergen, 19. september, 2023.
- Deep Learning. What is it? How do we do it? How can it be applied in medical imaging?, PRESIMAL Research School 2023, Tromsø, Norway, September 13–15, 2023
- Kunstig intelligens i helsesektoren: utdanning, arbeidsliv, forskning. Alrek Helseklynge, 10. august, 2023
- Kunstig intelligens i utdanning. Foredrag og paneldebatt under FIN/FØS-konferansen 2023, Bergen, 19. juni 2023.
- Medisinsk kunstig intelligens: begreper, muligheter, utfordringer, utviklingstrekk, fremtiden. Foredrag for Sykehusinnkjøp HF. 15. juni 2023.
- Kunstig intelligens i radiologi. Avslutningsseminar i To-Be-studien, Scandic Ørnen, Bergen, Norway, June 9, 2023
- Sarkomdagen 2023. Nytt fra forskning – Kunstig intelligens i stråleterapi og kreftdiagnostikk. Organisert av pasientforeningen Sarkomer Vest, Bergen, 6. mai 2023.
- Pasientnær KI i Helse Bergen: Aktiviteter, muligheter og utfordringer, Fellesmøte for pasientnær kunstig intelligens i nord, May 2, 2023
- Klok digitalisering og kunstig intelligens. Innlegg under Lederfrokost ved HVL, 31. mars, 2023
- AI og radiografens rolle. Bildebehandling— for alle med interesse (Norsk Radiografforbund), Thon Hotel Opera, Oslo, 1.-3. februar, 2023
- NRK P1 Sogn og Fjordane. Kor intelligent er kunstig intelligens?, 21. desember 2022.
- Foredrag om AI i medisinsk bildebehandling og diagnostikk ved Intelligente Bergen — kraftsenter for kunstig intelligens. 4. november 2022.
- Matematikk som et fagfelt, et språk og et verktøy. Foredrag under inspirasjonskveld for førsteårs ingeniørstudenter ved HVL, 20. november 2022.
- Foredrag om matematikk under Realfagsdagen 2022. 18. oktober 2022.
- Raskere MR med KI. Intervju i Hold Pusten 06 2020.
- AI could help with the next pandemic—but not with this one. Intervju i MIT Technology Review, 12. mars 2020.
- Tekprat: AI og maskinlæring. 19. september 2019.
- Utstilling om medisinsk AI ved Christiekonferansen 2019. 29. april 2019.
- Innlegg om kunstig intelligens i medisin under Springbrettet. 11. april 2019.
- Nå kan studenter lære om kunstig intelligens og medisin. Intervju i forskning.no, 15. februar 2019.
- Utstilling under EHiN 2018 i Oslo Spektrum. 14. november 2018.
- Foredrag om matematikk under Realfagsdagen. 31. oktober 2018.
- Eureka paneldebatt: Kunstig intelligens i helsevesenet. Mars 2018.
- Workshop om kunstig intelligens for elever ved Nordahl Grieg Videregående Skole under konferansen Framtid 2018: Kampen om virkeligheten: Fake, fakta eller fiksjon? 15. februar 2018.
- Innlegg om matematikk under Realfagsdagen. 26. oktober 2017
- Maskinlæring og kunstig intelligens, og hva matematikk har med slikt å gjøre. Foredrag under Forskningsdagene UNG 2017, 28. september 2017.
- Artikkel/kronikk om dyplæring i medisin i tidskriftet Helse, Medisin, Teknologi. 2017/4.
- Nytt norsk senter lærer datamaskiner å stille diagnose på sekunder. Intervju i Teknisk Ukeblad, 15. desember 2017
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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
- 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).
Artificial intelligence engineering for software engineers. Medical AI for medical and biomedical students.
- 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.
- 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
- Machine learning engineering
- Artificial intelligence
- Medical AI
- Data analysis
- Computational medicine
Personalized prognosis & treatment using Ledley-Jaynes machines: An example study on conversion from Mild Cognitive Impairment to Alzheimer's Disease