Our research profile
Our group is working on machine learning (ML) in a broad sense of the term, including statistical machine learning, more classic AI, and algorithm development. What brings us together is a shared research philosophy of diving deeply into real world domains to find ML challenges that have gone unnoticed by the ML field, to inspire novel methodology development. We refer to this as domain- and collaboration-inspired methodology development, and also as use-inspired basic research in machine learning. This is in the line of the Pasteur corner of Stokes' Quadrant Model of Scientific Research.
We typically form long-term, symbiotic collaborations with researchers and practitioners from other domains. This allows us sufficient focus to develop excellence in our own discipline of machine learning, while being exposed to ideas and inputs from other fields and collaborators in a way that ensures real world relevance and provides us with unique methodological inspiration. In this sense, our approach bears some resemblance to the field of action research within social science, where we involve ourselves deeply in the problems we are working on and let this interaction strongly influence our work. However, unlike classical action research, our focus is not on organisations or actors but on developing novel methodology, where it is the methods themselves that are shaped by deep interaction with domains and domain experts.
Several in the group draw methodological inspiration from life sciences. While our group is not restricted to life science, many have found this domain to provide rich opportunities for inspiration and long-term collaboration. Some group members have thus formed long-term collaborations with the HISP centre at IFI and with the Oslo University Hospital, and we consider these as central strategic partners to our group.
The research group has extensive collaborations across UiO and beyond, with a main focus in life science. The group develops several open source tools and publishes actively. To get an overview of the research performed in the group, please consult the web pages of the permanent staff. For supervision opportunities, see below.
Information for new master students
The SCML group supervises master students in the Computational Science (CS): Bioinformatics and Informatics: Programming and System Architecture (PROSA) study programmes.
Read about the experiences of three former master students in the SCML group. Look at the lists of current and former master students and their projects. If you are from the CS:Bioinformatics master program, you can look at our presentation for this program (12 Aug 2025). If you are from PROSA, you could go directly to the list of projects we offer.
Browse the available master thesis projects in the group and the completed and ongoing master projects. Note that the list may be outdated; new projects will usually be added in September/October each year. You may also contact the scientific staff (professors and associate professors) in the group to discuss a new project.
Consider the courses that we teach and the recommended courses for our master students.
