Statistical inference for intractable simulator-based models.
Likelihood-free inference and Approximate Bayesian Computation (ABC) are important computational inference methods for complex statistical models in a wide range of applications ranging from cancer evolution to modeling of ecosystems. We lead the Engine for Likelihood-Free Inference (ELFI) project, working both on inference algorithm development and applications of the methods to a multitude of challenging problems in collaboration with biologists, epidemiologists and physicists.
Statistical methods for bacterial population genomics.
The explosion of population genomics data has enabled entirely novel perspectives on evolutionary epidemiology. We develop tools for large-scale genome data analysis and apply them in collaboration with leading groups in pathogen genomics to gain insight to evolutionary processes of relevance for human and animal health.
Evolutionary epidemiology.
We lead several projects using whole-genome sequencing and metagenomics of bacteria to understand emergence and maintenance of genetic lineages in populations. We also study the evolution of phages and extra-chromosomal elements called plasmids to delineate success factors related to dissemination of AMR and virulence.