Title: Machine Learning Frameworks in Fluorescence Microscopy and Optics

Speaker: Mohamadreza Fazel, NIH/NCI

Abstract: Fluorescence microscopy and single-molecule methods have played a crucial role in shedding light on various subcellular mechanisms and providing insights into different subcellular structures and their functions, such as nuclear pore complexes and actin filaments. However, these techniques still face multiple challenges in data analysis, including sample photo-damage due to high photon budget requirements, rigorous noise treatment, model selection, and others. In this seminar, I will discuss my research on leveraging tools from Bayesian framework to address questions in single-molecule localization microscopy, particle tracking in the presence of optical aberrations.