+45 2843 9264
My main research interest is the structural connectivity of the brain. I am investigating the structural connections in the human and primate brain, estimated by diffusion weighted magnetic resonance imaging and tractography, by the use of machine learning tools. I am working on Bayesian modelling of structural brain connectivity, where I use machine learning tools to identify structures in connectivity graphs. These machine learning tools allow inference of the global structural brain connectivity in order to characterize changes in whole-brain connectivity in normal and abnormal subject populations.
Diffusion Imaging Group
MSc, Biomedical Engineering, Technical University of Denmark & Copenhagen University
BSc, Biomedical Engineering, Technical University of Denmark & Copenhagen University
2011 - 2012
Student Assistant at Dansk Lægemiddel Information Market Intelligence (DLIMI), København Ø, Danmark
Ambrosen, K.S., Albers, K.J., Dyrby, T.B., Schmidt, M.N., Morup, M. (2014, June). Nonparametric Bayesian Clustering of Structural Whole Brain Connectivity in Full Image Resolution. In Pattern Recognition in Neuroimaging (PRNI), 2014 International Workshop on (pp. 1-4). IEEE. doi: 10.1109/PRNI.2014.6858507.
Ambrosen, K.S., Herlau, T., Dyrby, T., Schmidt, M.N., Morup, M. (2013, June). Comparing Structural Brain Connectivity by the Infinite Relational Model. In Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on (pp. 50-53). IEEE. doi: 10.1109/PRNI.2013.22.