Shadi Jaradat

Shadi Jaradat is a Researcher at Queensland University of Technology (QUT) in Australia, affiliated with the School of Civil & Environmental Engineering. He holds a PhD in Civil & Environmental Engineering and a Master’s degree in Computer Science from the University of Queensland. His research focuses on data analytics, deep learning, NLP, and traffic safety, particularly leveraging AI techniques to enhance transportation safety through crash narrative analysis and multimodal data fusion.

Shadi’s PhD research explores Deep Natural Language Processing-based mining of crash narratives, aiming to uncover insights for improving traffic safety. His academic background bridges computer science and civil engineering, enabling interdisciplinary approaches to real-world challenges. Key research areas include crash severity analysis, cyberattack detection in industrial systems, and multimodal data applications for traffic safety.

His recent publications emphasize AI-driven methodologies for traffic safety, such as text mining for crash patterns, GPT models for multimodal analysis, and vision transformers for infrastructure inspection. Shadi collaborates with the Centre for Data Science at QUT, advancing data-driven solutions in transportation and cybersecurity domains.

While no scientific awards are listed, his work demonstrates impactful contributions to traffic safety and AI applications. Shadi actively engages in cross-cultural crash analysis and contributes to projects addressing urban mobility challenges through advanced analytics.