Greeting! I am Qilin (Chee-Lyn), a Senior Lecturer at Curtin University, Australia, within the Discipline of Computing. My role spans teaching and research, and my work sits at the intersection of artificial intelligence, machine learning, and computer vision with civil and structural engineering. My research develops data-driven models for intelligent infrastructure — spanning vision-based structural health monitoring, data-driven structural dynamics and blast simulation, digital twins, and, most recently, agentic AI and large language models for autonomous infrastructure monitoring — aiming to advance the state of the art and contribute to more efficient, effective, and sustainable engineering solutions.

News

• 2026 — Awarded an ARC DECRA Fellowship (A$510,000) for Next-Generation Agentic AI for Intelligent Infrastructure Monitoring.
• 2026 — Promoted to Senior Lecturer at Curtin University.
• 2025 — Awarded the National Road Safety Action Grant (A$641,436) for AI-assisted design of sustainable road barriers.
• 2025 — Awarded a Curtin Trailblazer EMCR Grant for an AI-empowered multi-modality digital twin.
• 2025 — Appointed Guest Editor for the Buildings special issue on AI-powered structural health monitoring.
• 2024 — Awarded an ARC Linkage Grant (A$415,380) on damage detection with infrastructure digital twins.
• 2024 — Co-led the CSIRO Next-Generation AI Graduate Program (A$350,000) with WA Police.

My research

I began my research journey as an MPhil student, where I delved into the foundational aspects of machine learning, exploring its theories and methodologies. This early experience laid the groundwork for my PhD, during which I focused on deep learning, investigating its potential to solve complex problems and push the boundaries of what machines can achieve in learning from data.

Upon completing my PhD, I transitioned my research focus toward the application of AI and machine learning techniques in the field of structural engineering. This shift was driven by a desire to bridge the gap between cutting-edge technology and real-world engineering challenges. My work now centers on developing and applying AI-driven solutions to problems such as data-driven structural dynamics simulation and vision-based structural health monitoring. These works are mostly conducted in collaboration with a world-class structural dynamics lab, CIMP (Centre for Infrastructural Monitoring and Protection).

By integrating AI/ML methodologies with engineering principles, I aim to contribute to the advancement of more efficient, accurate, and robust systems for monitoring and analyzing the health of structures. My research not only seeks to improve existing techniques but also to pioneer new approaches that leverage the power of machine learning to enhance the safety, reliability, and sustainability of our built environment.

My Background and history

I earned my PhD in 2020 and my MPhil in 2016, both from Curtin University, where I focused on advancing machine learning and deep learning techniques. My academic journey began with a BSc from Sun Yat-sen University in 2013, where I developed a strong foundation in computer science and engineering.

Immediately after completing my PhD, I joined the School of Electrical Engineering, Computing, and Mathematical Sciences at Curtin University as a faculty member. Since then, I have been actively involved in teaching and research, contributing to the development and application of AI and machine learning technologies, particularly in the context of structural engineering. My commitment to advancing these fields has been a constant throughout my academic and professional career at Curtin.