Ali Khaloo, Ph.D.

Engineer and researcher with a diverse background in structural engineering focused on developing frameworks to combine robotics, computer vision and artificial intelligence to conduct a quantitative condition assessment of infrastructure systems. Expert in utilizing Unmanned Aerial Vehicles (UAV) and 3D imaging techniques for automated as-built modeling followed by quantitative geometric analysis of generated models. Research interests include robotics and autonomous condition assessment of structures, 3D data processing, feature extraction, artificial intelligence and machine learning applications in civil engineering, pattern recognition, computer vision, data mining, computational geometry, image and video processing, Augmented Reality (AR) and Virtual Reality (VR) for structural integrity assessment, photogrammetry, and remote sensing.


A new and more robust multivariate statistical method for reliable estimation of normal vectors in noisy 3D point clouds with sharp features. Also, this work presents a new region growing method for context-free segmentation of unstructured 3D point clouds with outliers by utilizing locally adaptive spatial connectivity. 

Data-driven inspection of the Placer River Bridge (Girdwood, Alaska, U.S.A.) using UAVs, multi-scale camera networks and hierarchical DSfM for image-based 3D reconstruction. The bridge spans 85 m, making it the longest clear-span timber truss bridge in North America.