[September 2022] Maria Cywińska returned from NAWA Iwanowska scholarship in Max Plank Institute Bonn, Germany, where she visited visited PhD Maciej Wielgus and work on the project entitled „Bayesian and deep learning methods for interferometric data analysis: from nanoscale phase imaging to black hole shadow”. Project was based on the assumption that many concepts from radio interferometry easily translate to optical interferometry, as the underlying physical phenomena are essentially the same (wave interference, optical waves in phase imaging and mainly radio waves in Very Long Baseline Interferometry (VLBI) astronomy). Quantitative Phase Imaging techniques (i.e., interferometry, holographic microscopy, moiré techniques and fringe projection profilometry) are one of the most accurate techniques for measuring objects at the nano-, micro- and macro-scale and have the great advantage of being precise and non-contact. Due to the fact that in quantitative phase imaging methods the measurand is encoded in the registered intensity distribution (the phase of the registered fringe pattern), they additionally require a special phase demodulation method, which accuracy directly affects the accuracy of the performed measurement. The research and improvement the fringe pattern analysis algorithms (decoding the information about measurand) is an extremely important issue which is the subject of active research work of many international scientific groups. The algorithmic developments in the project was focused on Bayesian methods (dynamic nested sampling previously used in the data analysis for the first image of the black hole applied for optical interferometry data analysis) and modern deep learning approach.