Research project by Jacobus Dijkman
We introduce a method for learning a neural-network approximation of the Helmholtz free-energy functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly heterogeneous density profiles in a wide variety of external potentials. Read More ›
Research project by Teodora Pandeva
Unraveling the co-expression of genes across studies enhances the understanding of cellular processes. Inferring gene co-expression networks from transcriptome data presents many challenges, including spurious gene correlations, sample correlations, and batch effects. To address these complexities, we introduce a robust method for high-dimensional graph inference from multiple independent studies. Read More ›
Research project by Fiona Lippert
Weather radar networks provide wide-ranging opportunities for ecologists to quantify and predict movements of airborne organisms over unprecedented geographical expanses. We propose FluxRGNN, a recurrent graph neural network that is based on a generic mechanistic description of population-level movements across the Voronoi tessellation of radar sites. Read More ›
Research project by David Ruhe
We present a methodology for automated real-time analysis of a radio image data stream with the goal to find transient sources. Contrary to previous works, the transients we are interested in occur on a time-scale where dispersion starts to play a role, so we must search a higher-dimensional data space and yet work fast enough to keep up with the data stream in real time. Read More ›
Research project by Benjamin Miller
Peregrine is a new sequential simulation-based inference approach designed to study broad classes of gravitational wave signal. We show that we are able to fully reconstruct the posterior distributions for every parameter of a spinning, precessing compact binary coalescence using one of the most physically detailed and computationally expensive waveform approximants (SEOBNRv4PHM). Read More ›
Research project by Jim Boelrijk
A Bayesian optimization algorithm is developed for automated and unsupervised development of gradient programs. The algorithm was tailored to liquid chromatography using a Gaussian process model with a novel covariance kernel. The algorithm can operate in both the single- and multi-objective setting. Read More ›