My research activities are centred on the application of advanced data science, modelling, and analytical methods to address complex challenges in urban systems, transport, infrastructure, and environmental sustainability. My work integrates transport engineering, urban analytics, spatial data science, machine learning, and policy-oriented research to generate evidence-based insights for more sustainable and efficient cities.

At the City Futures Research Centre, UNSW Sydney, I contribute to the Australian Development Applications Intelligence project, which involves the collection, integration, management, and analysis of large-scale development application datasets from open data platforms and government sources. This research applies spatial analysis, natural language processing, and machine learning to examine urban development patterns, planning processes, and built environment transformation across Australia. Through this work, I aim to support more transparent, data-driven, and informed urban planning and policy decision-making.

My broader research background is strongly rooted in transport systems modelling and environmental impact assessment. During my doctoral research at the University of Trento, I developed advanced models for estimating exhaust and non-exhaust vehicle emissions by integrating traffic microsimulation, vehicle dynamics, travel behaviour modelling, big data analytics, and machine learning. This work contributed to a deeper understanding of how transport operations, driving behaviour, and urban traffic conditions influence air pollution and environmental outcomes.

I have also conducted research in discrete choice modelling, machine learning-based mode choice prediction, traffic simulation, spatial data mining, transport emissions analysis, and sustainable mobility. My research often combines traditional econometric approaches with modern artificial intelligence techniques to better understand travel behaviour, infrastructure performance, and urban system dynamics.

A key focus of my current and future research is the development of intelligent, scalable, and interpretable analytical frameworks that can support decision-making in transport planning, urban development, infrastructure investment, and environmental policy. I am particularly interested in how large and complex datasets, including administrative records, spatial data, transport data, and text-based planning information, can be transformed into meaningful insights for researchers, practitioners, and government agencies.

Overall, my research activities reflect a multidisciplinary commitment to using data, modelling, and technology to improve the way cities are planned, managed, and evaluated.