Moein Mirzaei holds a Bachelor’s and a Master’s degree in Structural/Earthquake Engineering, completed in 2012 and 2015, respectively. His Master’s thesis focused on the effects of randomly-distributed imperfections on the load-bearing capacity and progressive collapse behavior of multi-layered reticulated spatial trusses.

From 2020 to 2025, Moein held research positions at the International Institute of Earthquake Engineering and Seismology (IIEES) in Tehran, Razi University in Kermanshah (2021–2024), and Sahand University of Technology in Tabriz (2023-2025). He has contributed to several experimental and numerical studies, including the seismic performance and fragility assessment of steel structures with masonry infills, masonry infill-induced internal forces in steel frame structures, cyclic behavior of retrofitted battened columns, novel optimization-based pushover analysis procedures, and the seismic retrofit of reinforced concrete beam-column joints. His research portfolio also includes interdisciplinary collaborations addressing a range of structural mechanics challenges.

In 2025, Moein joined the STAND4HERITAGE project at the University of Minho as a PhD Candidate. His research focuses on the development of a machine learning-based framework for damage detection and classification in unreinforced masonry structures subjected to seismic loading. The project involves shake-table testing of dry-joint masonry specimens and employs advanced data acquisition tools such as Digital Image Correlation, high-resolution imaging, and accelerometers. The aim is to generate high-quality datasets and automate the identification of cracks, collapse mechanisms, and dynamic properties of masonry systems, contributing to data-driven strategies for the seismic resilience of heritage structures.