Andrej Dvornik

Designed pipelines for processing over 10 million-object cosmological datasets, automating dimensionality reduction, visualisation, and statistical analysis. Developed advanced mathematical models integrating machine learning (ML) and statistical inference. Employed Bayesian modelling, regression, and ML techniques to extract cosmological insights from noisy, high-dimensional data. Supervised multiple MSc and BSc theses and led working groups within international collaborations.

Built and utilised image-reduction pipelines to process petabyte-scale telescope imaging data, leveraging distributed computing frameworks within the Astro-WISE system. Applied machine learning methods (CNNs, self-organising maps, Gaussian processes) and advanced statistical models to analyse large astrophysical datasets. Designed and executed full-cycle research projects, including hypothesis formulation, data acquisition, model development, and result interpretation.