This research uses AI to detect subtle interactions between the Higgs boson and muons at the Large Hadron Collider. By refining large datasets, it aims to uncover how particles acquire mass at smaller scales. Confirming this interaction would deepen understanding of the Higgs field and fundamental physics.

AI can improve efficiency in humanitarian aid but risks undermining its moral foundation. Research shows donors perceive AI as lacking empathy, leading to reduced engagement and donations. The key challenge is balancing technological efficiency with human connection, ensuring that innovation supports rather than erodes the trust and compassion that sustain aid systems.

AI can answer religious questions, but it often blends traditions and provides incomplete answers. While specialized models exist, general models like ChatGPT can perform better due to broader training data. The key insight is that theology remains a human, dialogical process—AI should assist, not replace, human judgment and interpretation.

This research develops context-aware AI integrated with extended reality glasses, enabling systems to perceive and interact within real-world environments. Applications include language learning and memory support. Findings show such AI fosters more natural, collaborative interactions, enhancing human perception, memory, and decision-making beyond traditional screen-based interfaces.

This talk explores emotional resistance to AI through a personal storytelling project. It argues that AI adoption is an adaptive challenge tied to identity, not just technology. Using Robert Kegan’s framework, it demonstrates how testing limiting beliefs can reduce resistance, emphasizing that successful AI integration depends on addressing human concerns about autonomy, competence, and connection.

This research develops drones with soft robotic arms capable of safely grasping and transporting objects in challenging environments. By combining predictive modelling with visual feedback, it overcomes control challenges associated with soft materials. The work advances intelligent, adaptive aerial robotics for applications such as emergency delivery and hazardous environments.

This research uses nematode worms and machine learning to quantify changes in neuron structure linked to neurodegenerative diseases. By replacing subjective visual analysis with objective computational methods, it identifies structural abnormalities and improves understanding of disease mechanisms, supporting future advances in diagnosis and treatment.

This talk traces the devastation of the Black Death to highlight a modern crisis: antibiotic resistance. Misuse of antibiotics accelerates the rise of superbugs. Using AI and machine learning, the research identifies genetic resistance patterns and guides effective treatments, aiming to improve clinical decisions and prevent a return to a pre-antibiotic era.

This research develops reliable AI-powered drone systems to support New Zealand’s Predator Free 2050 initiative. By improving neural network calibration, uncertainty estimation, and robustness in challenging real-world conditions, the project aims to accurately detect invasive predators and better protect endangered native bird species.

This research presents a modular visuotactile robotic system for manipulating deformable objects such as cables, towels, and garments. Unlike rigid-object manipulation, deformables pose challenges due to occlusion, complex dynamics, and high variability. The system combines vision for global context and tactile sensing (GelSight) for precise local control, enabling tasks like cable tracing, cloth edge following, towel folding, and garment handling. It uses reactive control, learned dynamics (LQR), affordance models, and dense correspondence to generalise across tasks and objects. A key innovation is shifting from global state estimation to local, feedback-driven manipulation, improving robustness, efficiency, and real-world applicability in domains like manufacturing, healthcare, and assistive robotics.