Research Groups
The probability and statistical computing research unit is part of the Faculty of Informatics. The focus of the group is on network modelling, including random graphs, percolation, and inference. Graphs are an important paradigm for scientific research in the 21st century. The research programme spans the wide range of methodological developments and applied projects, from random graph models, sparse network model inference and systems biology, high-dimensional inference and inference of ODEs and SDEs.
Our research interests in the Scientific Computing Laboratory lie in the area of multiscale/multiphysics modelling and parallel large-scale simulations of biological systems. We focus on the development of new computational models and corresponding numerical methods suitable for the next generation of super computers. We are working on stochastic multiscale modelling of motion, the interaction, deformation and aggregation of cells under physiological flow conditions, biofilm growth, and coarse-grained molecular dynamics simulations, as well as the modelling of transport processes in healthy and tumour-induced microcirculation.
The research of the Advanced Computing Laboratory is centered around the topic of multicore and manycore algorithms for computing applications on emerging high-performance computing (HPC) architectures. Typically, we drive research towards extreme-scale simulations in computational algorithms, application software, programming, and software tools. We are currently involved in several HPC and computing research and simulation projects that develop methods and applications targeted at the next generation of petaflop/exaflop architectures. Interdisciplinary cooperation is a key to the work of this group, which functions as a link between various branches of computer science, computing technology, and application areas ranging from applied mathematics, to various branches of the engineering and natural sciences.
The research interests of the Statistical Science Laboratory encompass a wide range of advanced topics in statistics and machine learning. These include networks, point processes, Bayesian inference, high-dimensional and nonparametric statistics, tree algorithms, and graphical models. Additionally, the lab is dedicated to improving the interpretability, robustness, and fairness of statistical and machine learning methods. Significant efforts are directed towards counterfactual and Bayesian analysis to develop methodologies that are not only powerful and accurate but also transparent, reliable, and equitable. This comprehensive approach aims to create tools and frameworks that can be trusted and effectively applied in various domains.