I did both my BSc and MSc here at UiO. My BSc started as part of the Mathematics and Informatics programme (MAMI), where my coursework was spread evenly between mathematics, statistics and computer science. My bachelor project focused on fractals and imaging, and was supervised by Tom Lindstrøm.
I joined the relatively new Data Science master programme in 2019, where my coursework was themed around Bayesian data analysis, computational statistics and image processing. My masters thesis was initially part of the Bayesian Neural Network (BNN) group, and was written under the supervision of Odd Kolbjørnsen on the role of Invertible Neural Networks (INNs) in inverse problems for imaging, where we propose some new methods to bridge the gap between densely connected and convolutional feed-forward architectures and INN / NF (Normalizing Flow) structures.
I am (naturally) interested in Bayesian paradigms and probabilistic machine learning techniques, particularly generative modelling, normalizing flows, and encoder/decoder models. From a mathematical perspective, I am interested in representation theory, linear analysis, manifolds, and some differential geometry. In terms of applied mathematics and signal processing, I am currently very interested in frame theory, information theory, and generalised Fourier- and wavelet analysis. It is still early days for me here, but my research in the group will generally revolve around on Bayesian paradigms in deep learning, INNs and NFs, as well as work on incorporating parametric representations of image data.
On my spare time, I am interested in writing music, audio production, procedurally generated art, and recreational reading of various topics in physics and philosophy. I am very glad to join the group, and look forward to get to know everyone better.