See here for a catalog sketching possible master's thesis projects related to machine learning and medical data analysis.
Feel free to contact me if you're interested in more details.
Current MSc projects
Anders Benjamin Grinde and Bendik Johansen (2019–2021). Transfer learning in natural language processing
Malik Aasen and Fredrik Fidjestøl Mathisen (2019–2021). De-identification of medical images using generative adversarial networks
Completed MSc projects (main supervisor)
Adrian Storm-Johannessen and Sondre Fossen-Romsaas (2018–2020). Medical image synthesis using generative
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.
Using advanced computer vision techniques (graph convolutional neural networks),
and the Robot Operating System, the project investigated the potential of constructing robotic physical therapist
for patient rehabilitation. They presented part of their work at EHiN 2018 in Oslo Spektrum, and at
Christiekonferansen 2019. Their thesis is available here.
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.
Sathiesh presented part of his work at NVIDIA's GTC Europe 2018 in Münich, at EHiN 2018 in Oslo Spektrum, and at
Christiekonferansen 2019. His thesis is available here.
Sean Meling Murray (2017–2018). An Exploratory Analysis of
Multi-Class Uncertainty Approximation in Bayesian Convolutional Neural Networks. Sean developing and explored new techniques for
obtaining robust uncertainty estimates for deep neural networks. His thesis is available here.
Bendik Mathias Johansen and Kathinka Neteland (2019). Automating Reports on Water Consumption and Availability. A data science project
together with Bouvet and Bergen Vann. The students created a system for producing weekly reports on water
consumption and availability in Bergen, using Azure and Power BI.
- 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.
Feel free to contact me if you're interested in doing a BSc project, preferably in machine learning.
- DAT158: Machine learning engineering and advanced algorithms. Fall 2020.
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
Course website: DAT158ML.
is an elective course on computational medicine at the University of Bergen medical school.
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, motivated by e.g. "The convergence revolution".
Course website: ELMED219 @ MittUiB. Course code repository:
The course got some publicity during its first run. It was mentioned in the Norwegian government's
"Nasjonal strategi for kunstig intelligens", in Teknologirådet’s
report on “Artificial Intelligence: Opportunities,
Challenges and a Plan for Norway”, in a hearing
at Stortinget on “Langtidsplan for forskning og høyere utdanning 2019-2028”,
and the Faculty of
DLN PhD Course - A hands-on introduction to artificial intelligence in
computational biotech and medicine. I'm currently developing a new course in the Digital Life
Norway Research School portfolio. The course provides a practical, project-based, hands-on exploration of the state-of-the-art
techniques and software frameworks from machine learning and deep learning for solving real-world problems from biomedicine, biotech and
related fields. It will be a guided tour of a useful, interesting and important landscape, pointing out theoretical and application-oriented
gold-mines along the way. The course will be given for the first time at the end of 2020 / early 2021.
DAT255 - Practical deep learning. I'm currently developing a new
practically oriented, hands-on MSc course in deep neural networks. The course will be given in the Spring of 2021.
I'm also involved in Health Care Informatics
, a Continuing Education course at the University of
Bergen. Topic: Artificial intelligence for natural language processing and speech recognition.
- DAT158: Machine learning and advanced algorithms. Fall 2019.
- DAT259 – Medical data analysis and deep learning, spring 2020.
- DAT259 – Medical data analysis and deep learning, spring 2019.
- MAT106 – Videregående matematikk for elektroingeniører, spring 2019.
- DAT158: Machine learning and advanced algorithms. Fall 2018.
- DAT259 – Practical deep learning, spring 2018.
- MAT106 – Videregående matematikk for elektroingeniører, spring 2018.
- MAT100 – Grunnleggende matematikk for ingeniører, fall 2017, for students in the electrical engineering programs "Elkraftteknikk" and "Kommunikasjonssystemer"
- MAT106 – Videregående matematikk for elektroingeniører, spring 2016, for students in the electrical engineering programs "Elkraftteknikk" and "Kommunikasjonssystemer"
- BMED360 – In Vivo Imaging and Physiological Modelling, spring 2016
- MAT100 – Grunnleggende matematikk for ingeniører, fall 2015, for students in the electrical engineering programs "Elkraftteknikk" and "Kommunikasjonssystemer"
- MAT106 – Videregående matematikk for elektroingeniører, spring 2015, for students in the electrical engineering programs "Elkraftteknikk" and "Kommunikasjonssystemer"
- MAT100 – Grunnleggende matematikk for ingeniører, spring 2015, for students in the computer science engineering program "Dataingeniør"
- MAT100 – Grunnleggende matematikk for ingeniører, fall 2014, for students in the engineering programs "Produksjonsteknikk" and "Allmenn maskinteknikk"
I've created supporting e-learning material for multiple courses MAT106x,
financed by the Erasmus+ KA2 strategic partnerships
MedIm and HVL. To deliver these and other courses I set up the e-learning platform
AkademiX, hosted on AWS. The platform is not currently operational.
From 2011 to 2013 I gave the following courses at the Norwegian University of Science and Technology, mainly to students in engineering programs:
- TMA4125 – Matematikk 4, spring 2013
- TMA4100 – Matematikk 1, fall 2012
- MA0002 – Brukerkurs B, spring 2012
- TMA4100 – Matematikk 1, fall 2011
I have been a substitute lecturer in MAT212 – Functions of several variables
at the University of Bergen in 2010, and in Les algèbres de Hopf combinatoires en théorie quantique des champs perturbative
, Université de Strasbourg, 2009