I am a Senior Lecturer and ARC DECRA Fellow in the Discipline of AI & Data Science, School of Electrical Engineering, Computing and Mathematical Sciences (EECMS). My work sits at the interface of machine learning and structural engineering, developing data-driven methods for intelligent infrastructure: from machine-learning prediction of blast loading and graph neural network surrogates for structural dynamic response, to structural health monitoring with digital twins, vision-based vibration measurement and, most recently, agentic AI and large language models for autonomous infrastructure monitoring.

Background

• PhD in deep learning, Curtin University (2020, Chancellor's Commendation); MPhil in machine learning, Curtin University (2016); BSc in computer science, Sun Yat-sen University (2013)
• 50 publications, including eight articles in Engineering Structures and papers in IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems and Pattern Recognition
• Long-standing collaborator of the Centre for Infrastructural Monitoring and Protection (CIMP) led by Prof. Hong Hao

Research funding (nearly A$2 million as Chief Investigator)

• ARC DECRA Fellowship, 2026-2028: Next-Generation Agentic AI System for Intelligent Infrastructure Monitoring
• National Road Safety Action Grant, 2025-2027: AI-Assisted Design of Sustainable Road Barriers for Improved Road Safety
• ARC Linkage Grant, 2024-2027: Damage Detection and Quantification using Infrastructure Digital Twins
• CSIRO Next Generation AI Graduate Program with WA Police, 2025-2029: Effective and Ethical Use of AI and Data Science in Law Enforcement

Service and supervision

• Guest Editor for special issues of Buildings and Mathematics
• Early Career Editorial Board Member, Urban Lifeline (2024-2026)
• Reviewer for more than 30 journals across structural engineering, structural health monitoring and machine learning
• Currently supervising five PhD students, with two PhD completions as co-supervisor

Personal website: https://qilinli.github.io