Dr. Alexandra Moshou | Seismology Sensors | Sensing Technology Award 

Dr. Alexandra Moshou | Seismology Sensors | National and Kapodistrian University of Athens | Greece

Dr. Alexandra Moshou is a highly respected seismologist based in Athens, Greece, with extensive academic training and international research experience in earthquake science, mathematical modeling, and advanced seismic data analysis, recognized for her contributions to understanding earthquake source mechanisms and subsurface fault systems through both classical and data-driven approaches. Dr. Alexandra Moshou holds a doctoral degree in Seismology from the National and Kapodistrian University of Athens, where her doctoral research focused on the determination of earthquake source parameters through the development and programming of original software capable of extracting seismic characteristics from body-wave waveforms using teleseismic and local–regional datasets, complemented by a Master’s degree in Applied Mathematics that strengthened her expertise in wave propagation, scattering phenomena, and quantitative modeling, and a Bachelor’s degree in Mathematics that provided a rigorous analytical foundation for geophysical research. Dr. Alexandra Moshou has served as a Postdoctoral Researcher at the Hellenic Mediterranean University, contributing to advanced projects on four-dimensional spatiotemporal modeling of subsurface fault systems using deep learning applied to large-scale seismic datasets for fault-trap characterization, and has also been accepted as a postdoctoral researcher at the University of Pittsburgh, where she collaborated in international seismological research by processing, analyzing, classifying, and disseminating broadband and geophone field data for academic and institutional use, demonstrating strong engagement in both national and global research communities. Dr. Alexandra Moshou’s research interests include earthquake source physics, seismic waveform modeling, fault system characterization, seismic hazard assessment, applied mathematics in geophysics, deep learning applications in seismology, hydrocarbon-related fault-trap identification, and large-scale seismic data analytics.

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