My main research field is cosmology, in particular weak gravitational lensing. Using observational data from large galaxy surveys, I constrain cosmological models and infer information about dark matter and dark energy. To learn more about the topic, have a look at my (technical) review here, or check the video abstract on the companion web page.
I am interested in using cosmic shear, the distortion of galaxies by the large-scale structure in the Universe, to measure cosmological parameters. From CFHTLenS, we have obtained constraints on dark matter, dark energy and modified gravity parameters, using second- and higher-order statistics, tomography, 2D and 3D weak lensing techniques. See those papers for more details. The ESA space mission Euclid is expected to improve those constraints by orders of magnitude.
One particularly interesting weak-lensing observable are peak counts. This higher-order statistic is sensitive to the non-Gaussian aspects of the large-scale structure. Together with Chieh-An Lin, we have developed a new model prediction approach for peak counts. We have compared this model to N-body simulations, explored its stochastic nature for strategies to constrain cosmological parameters, and looked at different filtering techniques.
I have been developing and implementing the sampling method Population MonteCarlo (PMC) which is an efficient and massively parallelizable method to sample from an arbitrary posterior distribution. PMC readily provides an estimate of the Bayesian evidence. Recently, I have looked into the likelihood-free technique Approximate Bayesian Computation (ABC) and other likelihood estimation methods, together with my PhD student Chieh-An Lin.