Greeting! I am Qilin (Chee-Lyn), a lecturer at Curtin University, Australia, within the Discipline of Computing. My role encompasses both teaching and research, where I am deeply involved in exploring the intersection of machine learning and computer vision with structural engineering. My research focuses on developing innovative data-driven models for spatial-temporal simulation of physical processes in the field of structural engineering, aiming to advance the current state of the art and contribute to the development of more efficient, effective and sustainable engineering solutions.

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 collaborated with a world-class structral dynamics lab CIMP (Central for Infrastructral 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.