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 of the High-Dimensional Data Analysis Laboratory is focussed on the development and practical implementation of data analysis algorithms, inverse methods and time series analysis approaches. Application areas for the developed methods are problems from climate/weather research, computational finance and econometrics, biophysics, computational fluid mechanics, sociology and politology.