Mehdi Shafiei Joud

Research profile

Dr. Mehdi Shafiei Joud earned his Ph.D. in Satellite Geodesy, where he contributed to developing methods for relativistic geodesy using optical-atomic clocks and applied machine learning techniques to assimilate satellite data into geophysical models. His work also included Post-Glacial Rebound research, where he developed a pure gravimetric model for isostatic adjustment using unsupervised machine learning methods for data processing and ensemble techniques. He has worked extensively with satellite gravity missions, such as GRACE, to understand mass transport processes within the Earth system.

His research aims at integrating Synthetic Aperture Radar (SAR) interferometry and satellite gravity data with machine learning techniques to advance real-time environmental monitoring. A key aspect of his work is the development of Real-Time Earth Monitoring and Prediction Systems (REMPS), which utilize big data analytics and computational advancements to predict the effects of crustal stresses, seismic hazards, water resource changes, and sea-level rise on landscape evolution. These systems contribute to more accurate predictions of land subsidence, hydrological changes, and other climate-related risks, ultimately supporting climate resilience and infrastructure management.

Though his research spans diverse topics, the overarching goal is to improve the understanding of Earth system dynamics, particularly processes like mass transport and surface deformation. He has combined sophisticated satellite- and ground-based monitoring techniques with numerical modeling to link surface observations to underlying phenomena.

In addition to his research, he is passionate about educating the next generation of scientists, teaching geodesy and remote sensing. Outside of work, he enjoys coding to the sounds of electronic music in what he calls his "Cave of Forgotten Dreams," where creativity meets scientific discovery.