I’m professor of Statistical methods in Bioinformatics at the Section of Biostatistics, Department of Public Health, University of Copenhagen.

My main research focuses on

  • development of statistical methods for analysis of various aspects of high-dimensional data - in particular data from various types of *omics experiments and large-scale register data.
  • methods for integrative data analysis of fused data from multiple platforms, image analysis of radiology pictures, statistical genetics, causal structure learning from large-scale observational register data, life-course epidemiology, and analysis of network data for gene, social, or familial networks.
  • developing methods for causal discovery for observational (in particular longitudinal) data.

Generally, I explore the possibilities of synergistic collaborative research between various fields in order to provide inspiration and creativity for statistical developments as well as novel analyses in applied fields.

Ongoing projects

Causal discovery

Inferring the underlying causal structure from observational data with temporal or external information, and measure the impact of misrepresenting DAGs.

Trend analysis

Bayesian trend analysis based on Gaussian processes to infer the probability that a change in trend has occurred.

Stats communication

Communicating statistics and interpreting results from statistical results in research papers.

Trajectory analysis

Using machine learning for latent class trajectory analysis in epidemiology for unsupervised hierarchical subclassification of eating disorders.

Sports Statistics

Analysis of data related to sports performance, such as team stats and game outcomes. Techniques involve the study of trends and patterns in the data to make predictions about future performance, player injuries or coaching decisions.

Image analysis of joints

Artificial intelligence in veterinary diagnostic imaging to classify fragments in joints. Measuring the added benefit of augmented decision-making and quality control.

Integrative data analysis

Developing new statistical tools for integrative data analysis to simultaneously analyze multi-platform biological data.