Probabilistic Inference Lab (PIL)
Uncertainty is an inherent part of all scientific activity and the generally agreed framework for handling uncertainty is the use of probability. Our lab focuses on enabling and making inferences with the aid of probabilistic models, with a wide range of applications in life sciences, technology and engineering.
About the group
Computational inference methods for complex statistical models are important in a wide range of applications ranging from cancer evolution to modeling of ecosystems. We work both on inference algorithm development and applications of the methods to a multitude of challenging problems in collaboration with biologists, epidemiologists and physicists. Our vision is to enable better and faster solutions to scientific inference problems and to aid development of future technologies targeting improved human health and resource management.
Long term objectives
- Development of fast and robust generic algorithmic tools for inference in simulator-based statistical models
- Fast inference methods with applications to genomic models in evolutionary epidemiology related to bacteria and viruses
- Statistical machine learning for Big Data