This research develops an ultra-low-power, battery-free newborn monitoring system for under-resourced hospitals. Using on-device artificial intelligence and energy harvesting, it continuously detects signs of distress while protecting patient privacy. The technology aims to support overstretched nurses, enable earlier intervention, and reduce preventable newborn deaths worldwide.

This research examines why AI adoption often fails in medium-sized businesses despite significant investment. By interviewing employees and leaders, it develops a practical framework that treats AI as a new colleague requiring onboarding, trust, and cultural integration. The findings will help organisations implement AI more successfully and improve long-term adoption.

This research uses artificial intelligence to analyse immune-system data and predict vaccine effectiveness. By identifying early biological signals associated with strong, long-lasting immunity, the work aims to improve vaccine design, personalise vaccination strategies, and support development of universal vaccines capable of protecting against rapidly evolving infectious diseases.

This thesis examines who turns to AI for mental health support, rather than whether AI can be a therapist. Drawing on TherapyGPT forum analysis and ongoing experiments, the research identifies fear of judgment, trust in AI and past therapist failures as possible drivers of AI therapy use.

This research explores how heterogeneous AI agents can establish common ground during collaboration. By separating communication and action into distinct decision-making policies, agents can engage in micro-conversations that create shared understanding. The work aims to improve teamwork among diverse robots and support future human-AI collaboration in complex environments.

This project developed AI Care, a voice-based caregiving system for people with early-stage Alzheimer's disease. Unlike conventional voice assistants, it uses caregiver-maintained medical records to provide personalised, safety-aware support. By adapting to users rather than requiring users to adapt to technology, AI Care aims to extend safe, independent living at home.

This research develops brain-inspired computer chips using memristors, devices that can store and process information simultaneously like biological synapses. By enabling in-memory computing, the technology reduces energy consumption while supporting applications such as autonomous robots and image processing. The work advances efficient hardware for future artificial intelligence systems.

This research uses artificial intelligence and machine learning to analyze light from distant exoplanets. By interpreting atmospheric spectral signatures, it aims to identify potentially habitable worlds and search for signs of life beyond Earth. The work supports future space missions designed to answer one of humanity’s oldest questions: Are we alone?

This research investigated whether AI-guided handheld ultrasound can help diagnose deep vein thrombosis (DVT) in primary care. Through a systematic review, a clinical study involving 565 patients, and stakeholder interviews, the research found promising results but highlighted challenges involving image quality, accountability, and integration into NHS healthcare systems.

This PhD defense presents research at the intersection of machine learning, reinforcement learning, social learning, affective computing, and human-AI interaction. The thesis is that social learning is a powerful mechanism for intelligence and explores how AI agents can learn from one another and from humans. Projects include intrinsic social influence rewards for multi-agent coordination, communication protocols emerging through influence, conversational agents trained from implicit human feedback such as sentiment, generative models improved through facial-expression feedback, and personalized well-being prediction from behavioral and physiological data. The thesis concludes that socially informed learning can improve coordination, adaptability, and human alignment.