Rate My Professor Qilin Li

Dr. Qilin Li is a Senior Lecturer and ARC Discovery Early Career Researcher Award (DECRA) Fellow at Curtin University, Australia. He serves in the Discipline of Computing within the School of Electrical Engineering, Computing and Mathematical Sciences, Faculty of Science and Engineering. Qilin Li obtained his BSc degree in Computer Science and Engineering from Sun Yat-sen University in 2013. He then pursued higher studies at Curtin University, earning an MPhil degree in 2016 with a focus on machine learning theories and methodologies, and a PhD degree in 2020 centered on deep learning applications to complex problems. Immediately after his PhD, he joined Curtin University as a Lecturer in 2020 and has progressed to Senior Lecturer, actively contributing to teaching and research at the intersection of artificial intelligence and structural engineering.

Dr. Li's research specializes in developing AI-driven solutions, including machine learning and computer vision techniques for structural engineering applications such as vision-based structural health monitoring, data-driven spatial-temporal simulations of physical processes, blast loading prediction, and structural dynamic responses. He collaborates with the Centre for Infrastructural Monitoring and Protection (CIMP) at Curtin University. His efforts have attracted funding from the Australian Research Council (ARC), including the DECRA Fellowship and support for projects like the Next-Generation Agentic AI System for Intelligent Infrastructure Monitoring. Key publications include "A comparative study on the most effective machine learning model for blast loading prediction: From GBDT to Transformer" (Engineering Structures, 2023), "Machine learning prediction of BLEVE loading with graph neural networks" (Reliability Engineering & System Safety, 2024), "Prediction of BLEVE blast loading using CFD and artificial neural network" (Process Safety and Environmental Protection, 2021), "Machine learning prediction of structural dynamic responses using graph neural networks" (Computers & Structures, 2023), and "Monocular vision based 3D vibration displacement measurement for civil engineering structures" (Engineering Structures, 2023). These works highlight his impact on advancing sustainable and reliable engineering practices.