The main subject of the workshop was sampling and non-sampling methods in cosmology. The purpose of the workshop was to bring together experts on cosmological data analysis, with the focus on the sampling methods in cosmology, and their alternatives. Some of the topics for discussion were the current state of the art statistical and data analysis methods at various stages of data processing, from image processing or time stream reductions to low order statistics, to cosmological parameters. Also dicussed were methods of systematics cleaning (foregrounds, extinction etc.), joint map and cosmology analyses (Gibbs and Hamltonian samplers and alternatives) etc.
Speaker(s)
Yu Feng
My study in cosmology focuses on the formation of galaxies in the large-scale structure of the Universe. Cosmology is a data-driven science. I develop the necessary software and tools that can efficiently handle these data on platforms from laptops to supercomputers, including (1) massively parallel software to solve gravity and hydrodynamics on tens of thousands of computing nodes, (2) tools to visualize density estimation of large particle datasets with hundreds of billions of particles, and (3) algorithms to understand the clustering of galaxies. I also contribute to open source data science software projects as a user developer. I strongly believe in the power of adequate tools in data-driven science research.