Hamed Khorosgani

Hitachi Global Storage Technologies (United States)

Papers

Total Citations

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About

Hamed Khorosgani is a researcher at the forefront of industrial AI, specializing in automated visual inspection, predictive maintenance, and asset health management. His work addresses a critical industry challenge: preventing equipment breakdowns and downtime through intelligent, data-driven monitoring. Khorosgani’s major contribution lies in developing AI-based detection models that transform traditional visual inspection into a guided, automated process, streamlining maintenance, quality control, and safety protocols. His 2021 paper, "Guided Visual Inspection enabled by AI-based Detection Models," has garnered 3 citations, laying foundational work for integrating computer vision with industrial reliability engineering. By bridging the gap between theoretical machine learning and practical industrial applications, Khorosgani’s research offers scalable solutions for proactive asset management. His achievements include advancing the use of deep learning for real-time defect detection, reducing human error, and enabling cost-effective maintenance strategies. For students and researchers in industrial engineering and AI, Khorosgani’s work demonstrates how intelligent systems can revolutionize traditional inspection workflows, making industries safer and more efficient.