My research brings together software engineering, artificial intelligence, cybersecurity, data science, geospatial information science, and data governance. I am interested in research that addresses practical problems while also contributing new methods, tools, or evidence.
Some of my current research projects include:
Geospatial Privacy and Indigenous Data Sovereignty. Geographic information can be highly sensitive because it may reveal the location of people, homes, cultural sites, natural resources, or activities. This project investigates ways to protect location information while retaining enough detail for legitimate research, planning, environmental monitoring, and community decision-making. It also considers how technical systems can support Indigenous and Māori data sovereignty, including questions of authority, consent, provenance, access, and responsible sharing. MapSafe is one of the platforms through which this work is being explored.
Privacy-Preserving Spatial and Mobility Data. Location datasets are increasingly used to study movement, transport, health, environmental change, and public services. However, even apparently anonymous points and movement patterns can sometimes be linked back to individuals, households, or places. This project examines privacy-preserving approaches for spatial and mobility data, together with the trade-offs between privacy protection and analytical usefulness. The work includes the study of different data contexts, privacy risks, evaluation measures, and practical ways of helping data custodians make informed release decisions.
Responsible Artificial Intelligence for Student Wellbeing. Artificial intelligence may offer new ways to provide timely and accessible support to students, but wellbeing applications must be designed with care. This project investigates culturally grounded and human-centred AI systems that can respond appropriately to different student needs while recognising the importance of human relationships and professional support. The research considers personalisation, communication style, cultural context, safety, privacy, trust, and the ways students understand and use AI-based support. Earlier work in this area examined trainer-guided, multi-persona conversational support for student wellbeing in a Pacific context.
Cybersecurity, Data Protection, and Human-Centred Security. Cybersecurity is not only a technical problem; it also involves human behaviour, organisational practice, and the way security controls are experienced by users. This project area covers secure software and systems, network and application security, privacy and data protection, vulnerability analysis, ethical hacking, incident response, cyber resilience, and the responsible use of artificial intelligence in security. It also examines how people recognise threats, make security decisions, respond to warnings, and participate in creating safer digital environments.
Artificial Intelligence and Data Science for Software Engineering. Modern software projects generate large volumes of information through source code, issue trackers, requirements, reviews, discussions, and user feedback. This project investigates how artificial intelligence, machine learning, natural language processing, and data-mining techniques can help software teams make sense of this information. The focus is on extracting useful patterns, supporting software decisions, understanding emerging needs, and developing approaches that remain transparent and useful to practitioners.
Open-Source Software Communities and Decision-Making. Open-source communities depend on distributed collaboration, discussion, review, and informal decision-making. This project examines how developers express preferences, explain decisions, influence one another, and participate in software communities. It also considers how the structure and diversity of these communities may affect collaboration, inclusion, and the evolution of software projects. The research combines empirical software engineering with data analysis and, where appropriate, qualitative investigation of developer experiences.
These themes are broad enough to support a range of Master’s and PhD research projects in computing, software engineering, cybersecurity, data science, GIScience, information systems, and related fields. The eventual focus of a project is shaped by the research gap, the student’s background, the needs of the research setting, and the requirements of the relevant programme.