This research investigates asthma’s underlying mechanisms, focusing on airway fibrosis and the extracellular matrix. Using Raman spectroscopy, researchers generate molecular “barcodes” of lung tissue. Artificial intelligence is then applied to analyze complex data, aiming to identify key biological drivers of asthma and move beyond temporary treatments toward deeper understanding and potential long-term solutions.

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.

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 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.