I mainly work with methods for analysing datasets which have some complex dependence structure across space and time. Modern approaches within Bayesian statistics and machine learning make this possible with flexible probabilistic models which can also correctly handle uncertainty when making inferences and predictions. My research focuses on developing these methods and making them computationally efficient for big datasets. In particular I work with spatial models in neuroimaging, for fMRI images which typically contain millions of datapoints.
My main supervisor is professor Mattias Villani and my co-supervisor is associate professor Anders Eklund. I am part of the Machine Learning Research Group and the Bayesian Neuroimaging Research Group at IDA. I finished my M.Sc. in Engineering Mathematics at Lund University in 2012. From 2012-2014 I worked in Stockholm at the Central bank of Sweden and If P&C Insurance, before joining LiU in 2014. During the second half of 2017, I worked at the University of Edinburgh, UK, as a visiting scholar.