Morteza Saberi

Papers

Total Citations

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About

Morteza Saberi is a researcher whose work bridges the frontiers of big data analytics and structural health monitoring. His key research areas include stratification theory, data-driven decision-making, and inverse problem-solving in engineering systems. Saberi made a major contribution with his 2018 paper "The concept of stratification and future applications" (18 citations), which introduced a novel framework for handling heterogeneous data in large-scale environments. This work was extended in his 2017 study "Targets of Unequal Importance Using the Concept of Stratification in a Big Data Environment" (11 citations), demonstrating how stratified sampling can optimize resource allocation in complex systems. Most recently, Saberi authored a comprehensive 2025 review on impact force identification (5 citations), which systematically evaluates state-of-the-art methodologies for this critical inverse problem. The review establishes a taxonomy of techniques used in aerospace, automotive, civil infrastructure, and robotics applications, providing a valuable resource for researchers tackling force reconstruction challenges. His work is particularly notable for connecting theoretical stratification concepts with practical big data implementations, offering engineers and data scientists actionable frameworks for handling unequal importance across diverse datasets. Saberi's research continues to influence how complex systems are monitored and optimized in data-rich environments.