The Lagergren lab is concerned with method development in machine learning and probabilistic modeling as well as application of the developed methods. The goal is to facilitate investigations of developmental, temporal, and spatial aspects through analysis of cutting edge biological data that has single cell resolution, in some cases also carrying spatial information. Methodologically, the projects focus on advanced methods for computational inference such as Expectation Maximization, Sequential Markov Chain, Variational Inference, Markov Chain Monte Carlo (MCMC), and Particle MCMC methods. It is also important for us to obtain efficient implementations of our methods by taking advantage of modern computational technology.
External website: https://lagergrenlab.org/