We develop methods for efficient probabilistic inference in complex modelling problems. Our main applications are in developing statistical methods for modelling molecular biology time series using Gaussian processes, as well as methods for quantitative analysis of sequencing data (e.g. RNA-sequencing and metagenomic sequencing). Developing methods for privacy-aware modelling is an emerging focus area. We are a part of the Statistical Machine Learning and Bioinformatics group at HIIT.