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About

David Lattanzi is a prominent researcher at the intersection of robotics, computer vision, and civil infrastructure inspection, whose work has significantly advanced the automation of structural health monitoring. His most influential contribution, a comprehensive 2017 review of robotic infrastructure inspection systems, has garnered 259 citations and stands as a foundational reference for researchers and practitioners seeking to understand the landscape of automated inspection technologies. Lattanzi's research consistently tackles the practical challenges of structural assessment — reducing costs, minimizing human risk, and improving data quality — through innovative applications of emerging technologies. His technical portfolio spans 3D scene reconstruction from monocular imagery for bridge inspection, hue-assisted analysis of color point clouds for defect detection, and the application of convolutional neural networks to acoustic monitoring of robotic manufacturing facilities. This latter thread of research reflects his expanding interest in semi-supervised and unsupervised machine learning approaches for industrial process monitoring. Across his career, Lattanzi has built a coherent research vision: replacing dangerous and expensive manual inspections with intelligent, autonomous systems capable of perceiving, interpreting, and reporting on infrastructure condition with minimal human intervention — a contribution of growing importance as aging infrastructure demands scalable, reliable assessment solutions.