Research Areas

Research Areas

Research Areas

The Centre focuses on the following core research specialisations:

Immersive visualisation, spatial computing, and human-AI exploration

  • Immersive and cross-reality visualisation for exploring scientific and complex data across screens, XR devices and physical spaces;
  • Spatially anchored data intelligence, including spatial positioning, coordinate registration, spatial relation modelling and digital-twin environments;
  • Human-AI and human-agent interaction for communication, interpretation, trust and collaboration with intelligent systems; and
  • Multimodal interaction, nonverbal behaviour analysis, adaptive interaction and embodied conversational agents.

Cultural intelligence, XR, and immersive education

  • AR- and AI-powered multimodal experiences for cultural materials, digital humanities and cultural narrative systems;
  • Virtual, augmented, mixed and extended reality for cultural heritage, digital heritage, virtual museums and heritage tourism;
  • XR and AI-enhanced learning environments that make abstract knowledge visible, interactive and collaborative; and
  • Technology-mediated communication and intelligent interaction for cultural, educational and public-engagement contexts.

 AI for healthcare, industry, energy, and physical-world intelligence

  • AI-based medical analysis, smart sensing, image processing and multimodal health data intelligence;
  • Industrial AI, virtual engineering, modelling and simulation, hybrid twins and enterprise-facing digital transformation;
  • Energy data intelligence, photovoltaic-system modelling, performance prediction, fault diagnosis and low-carbon applications; and
  • Physical-world and embodied intelligence, including autonomous navigation, multimodal perception, robotic planning, USV algorithms, and human-in-the-loop multi-robot systems.

AI in education, data communication, and remote collaboration

  • AI-assisted programming education, educational technology, learning analytics and responsible AI use in teaching and learning;
  • Network analysis, sentiment analysis, AI-enabled app development and machine-learning methods for applied decision-making;
  • Data-driven interdisciplinary research, including empirical software engineering, evidence synthesis and cross-domain research methodology;
  • Socio-technical systems research, spanning cross-cultural requirements engineering, agent-based modelling, cybersecurity education and phishing awareness; and
  • AI tools for remote collaboration, large-space awareness, CSCW, technology-mediated communication and human-AI teamwork.