This research develops microscopic copper wire "bridges" that improve heat transfer between computer chips and cooling systems. By reducing chip temperatures by around 3°C, the technology can lower data centre cooling energy by approximately 10%, improving efficiency and supporting more sustainable AI infrastructure.

This research develops scalable motion-planning algorithms that enable large teams of robots to work together safely and efficiently. By combining machine learning with search algorithms, the work delivers both speed and reliability, supporting applications from automated warehouses to disaster response, infrastructure repair, and future space exploration.

This research has developed an electronic nose that combines gas sensors with machine learning to detect food spoilage and hidden allergens. By recognizing unique scent signatures more accurately than the human nose, the technology could improve food safety, prevent allergic reactions, reduce food waste, and eventually be integrated into everyday devices.

This research combines galaxy simulations with machine learning to study the invisible gas surrounding galaxies. By training a neural network to interpret astronomical observations, the project creates a public tool—the Circumgalactic Dictionary—that enables previously impossible measurements, advancing our understanding of galaxy evolution and the origins of stars, planets, and life.

This research investigates how AI-generated dating profiles influence romantic attraction. An experimental study found that people were less willing to date someone they believed had used AI than someone who received similar help from a friend, suggesting AI-assisted dating may undermine authenticity and reduce social appeal despite its growing popularity.

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.