Rate My Professor Senjian An

Dr. An's research specializations encompass deep learning, computer vision, and image processing, with recent emphases on structural health monitoring, 3D vision-based displacement measurement, anomaly detection, and visual analysis for applications such as grain quality assessment and vessel trajectory monitoring. He has co-authored numerous peer-reviewed publications in prestigious venues. Key works include 'Deep residual network framework for structural health monitoring' (2020), 'Monocular vision based 3D vibration displacement measurement for civil engineering structures' (2023, Engineering Structures), 'Structural damage identification by using physics-guided residual neural networks' (2024, Engineering Structures), 'Out-of-plane full-field vibration displacement measurement with monocular computer vision' (2024, Automation in Construction), 'Structural damage detection and localization via an unsupervised anomaly detection method' (2024, Reliability Engineering & System Safety), 'DIMMC: A 3D vision approach for structural displacement measurement using a moving camera' (2025), and 'GrainMIL: Automated Grain Damage Classification from Weak Labels via Deep Multiple Instance Learning' (2026). His scholarship has accumulated over 5,200 citations on Google Scholar. Dr. An has participated in Australian Research Council (ARC) funded projects, including a 2022 Discovery Project on structural health monitoring with colleagues such as John Curtin Distinguished Professor Hong Hao and Professor Ling Li. He also supervises research students in relevant fields.