My research program is organized around five mutually reinforcing streams that connect foundational questions about human-centred AI, human-AI interaction, and human behaviour with the design, development, and evaluation of intelligent technologies for public and social good. I examine how AI-enabled systems can sense, interpret, adapt to, and support human needs, states, behaviours, goals, and context while remaining understandable, trustworthy, controllable, inclusive, and safe.
01
Investigates how people interact with, understand, trust, and collaborate with AI-enabled systems, including LLM-based, generative, and agentic AI, with attention to explainability, user agency, privacy, safety, inclusion, and responsible deployment.
02
Develops intelligent systems and interventions that use dynamic user modelling, contextual information, and behaviour change strategies to tailor support to users’ evolving characteristics, states, preferences, behaviours, and situations, while keeping people aware of, and in control of, how systems adapt to them, and persuading without coercion or deception.
03
Explores multimodal AI and interaction through wearable and multisensing technologies, eye gaze, voice, gesture, haptics, augmented/virtual/mixed reality, human activity recognition, context-aware systems, and AI-assisted robotic interaction, to sense and interpret human states and context and support natural, accessible interaction, with people in control of what is sensed and shared.
04
Examines how behavioural, emotional, social, motivational, learning, and contextual factors shape people’s interactions with intelligent technologies and their responses to digital interventions. Designs theory-grounded interventions together with the people they are meant to serve, through user-centred, participatory, and co-design approaches, using quantitative, qualitative, mixed-methods, and review-based evidence to inform design and evaluation.
05
Applies and evaluates human-centred AI across health and well-being, education and learning, safety and security, environmental sustainability and climate adaptation, mobility and accessibility, health equity, and community-engaged and culturally responsive contexts, developed and evaluated in partnership with communities, industry, and public organizations.
My work combines user-centred, participatory, and co-design approaches with machine learning, deep learning, natural language processing (NLP), large language models (LLMs), generative and agentic AI, multimodal AI, and software and cloud systems, while encompassing mixed-methods empirical evaluation, conceptual research, and evidence synthesis. Throughout, I follow responsible and trustworthy AI practices: explainability, privacy, safety, user agency, and inclusive, ethical design.