Morten Dæhlen

简介

I am a professor of informatics, primarily working on interdisciplinary issues related to artificial intelligence, machine learning, and knowledge representation. My academic background is in computational science, with an emphasis on numerical analysis, geometric modeling, and machine learning. Within these fields, I have been particularly interested in the modeling and representation of large datasets for machine learning and visualization purposes. In recent years, I have been especially engaged in research related to digitalization and the green transition.

代表成果

• Scientific articles and book chapters
• Øvrelid, Egil; Bygstad, Bendik; Ludvigsen, Sten Runar & Dæhlen, Morten (2023). Dual Digitalization: A Framework for Digital Transformations of Higher Education. In Pinheiro, Romulo; Tømte, Cathrine Edelhard; Barman, Linda; Degn, Lise & Geschwind, Lars (Ed.), Digital Transformations in Nordic Higher Education. Palgrave Macmillan. ISSN 9783031277580. p. 53–74. doi: 10.1007/978-3-031-27758-0_3. Full text in Research Archive
• Bygstad, Bendik; Øvrelid, Egil; Ludvigsen, Sten Runar & Dæhlen, Morten (2022). From dual digitalization to digital learning space: Exploring the digital transformation of higher education. Computers & Education. ISSN 0360-1315. 182. doi: 10.1016/j.compedu.2022.104463. Full text in Research Archive Show summary Inspired by the fast digitalization during the Covid-19 crisis, we investigate a key aspect of digital transformation of higher education – the emergence of a digital learning space. In developing our analysis, we focus on two streams of digitalization in higher education; digitalization of education and digitalization of subjects. We call this dual digitalization, which has been an obstacle for digital transformation of the sector, and made it challenging to develop a shared digital space. Our research question is, how can we develop a shared digital learning space in higher education? We conducted our study at the University of Oslo, where we analyzed three phases of digitalization. We identified three underlying forces of the digital learning space. First, the alignment of digital education and digital subjects provided a technical foundation. Second, the digital learning space was enacted and harnessed by redefinition of roles between students and teachers, allowing for new and deeper learning forms. And third, the digital learning space enables universities to transcend the physical and institutional borders, and engage in interactions with the broader society.
• Dyken, Christopher; Dæhlen, Morten & Sevaldrud, Thomas (2009). Simultaneous curve simplification. Journal of Geographical Systems. ISSN 1435-5930. 11(3), p. 273–289. doi: 10.1007/s10109-009-0078-8. Full text in Research Archive
• Hjelle, Øyvind & Dæhlen, Morten (2005). Multilevel Least Squares Approximation of Scattered Data over Binary Triangulations. Computing and Visualization in Science. ISSN 1432-9360. 8(2), p. 83–91. Full text in Research Archive
• Dæhlen, Morten; Fimland, Morten & Hjelle, Øyvind (2001). A Triangle-based Carrier for Geographical Data. In Halls, Peter (Eds.), Spatial Information and the Environment. Taylor & Francis, London. ISSN 9780415253628. p. 105–120. Full text in Research Archive Show summary In this paper we discuss a framework for representation and maintenance of multi-resolution terrain and network data. We use a triangle-based representation of the terrain surface, and representations of curve networks (river systems, roads, railroad, etc. are integrated into the triangle-based representation. We introduce a data model for the representation and we discuss issues connected to the construction of the integrated representation of data. Several examples are provided.
• Dæhlen, Morten; Lyche, Tom Johan; Morken, Knut; Schneider, Robert & Seidel, Hans-Peter (2000). Multiresolution analysis based on quadratic Hermite interpolation on triangles. Journal of Computational and Applied Mathematics. ISSN 0377-0427. 119, p. 97–114. Full text in Research Archive
• Arge, Erlend; Dæhlen, Morten; Dæhlen, M. & Tveito, Aslak (1997). Data Reduction for Piecewise Linear Curves. In Tveito, Aslak & Dæhlen, Morten (Ed.), Numerical methods and software tools in industrial mathematics. Birkhäuser, Boston. ISSN 9780817639730. p. 347–364. Full text in Research Archive