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 uses drone imagery and a hybrid AI model to classify rangeland cover as green vegetation, dead vegetation, or bare soil. Combining two neural network approaches achieved 96% accuracy while requiring only simple, low-cost sensors. The method enables fast, large-scale monitoring to combat invasive shrubs and support sustainable land management.

This project uses hive sound recordings and machine learning to detect early signs of bee swarming. By identifying acoustic differences between swarming and stable colonies, the system predicts swarming with 93% accuracy. This enables beekeepers to intervene early, prevent colony loss, and even create new healthy colonies.

This study evaluated a PointNet++ deep learning model for binary classification of Pinus sylvestris and Quercus pyrenaica using only LiDAR 3D point clouds. A balanced dataset of 160 trees achieved 91% accuracy, showing that geometric features alone can effectively discriminate species, highlighting the potential of lightweight AI models for forest inventories.

Inspired by biological reproduction, this research uses evolutionary algorithms to evolve mathematical equations that describe physical systems. Unlike black-box AI, these models are transparent and adaptable. By combining evolution with graph neural networks, the approach improves simulations for applications such as traffic control, robotics, and engineering design.

My research uses field images to predict crop yield, leveraging machine learning techniques to extract patterns and features correlating yield.  These features include plant health indicators, growth stages,  or canopy coverage. I am particularly interested in using these features to develop models  that improve the accuracy of yield prediction, helping farmers make  data-driven decisions. My approach considers temporal changes in the crop, capturing how its characteristics evolve. My work contributes to precision agriculture, a field that seeks to optimize resource use, increase productivity, and promote sustainability in farming. My research has the potential to transform traditional agricultural practices by integrating advanced AI methods.

This research investigates the limitations of AI-driven enterprise resource planning systems in multinational corporations. Using mixed methods, it examines ethical risks, data integrity, training gaps, and system migration challenges. The study aims to help organisations implement ERP systems more effectively, reducing financial losses while critically evaluating whether AI delivers its promised efficiency.

As generative AI reshapes the advertising industry, this research shows creativity is not replaced but redistributed. Through interviews and immersive fieldwork, a four-stage framework—readiness, co-creativity, validation, and execution—reveals how humans and AI can collaborate to amplify creative potential rather than diminish it.

This research examines how AI is used in NHS radiology and challenges claims that it will replace radiologists. Instead of full automation, AI supports clinicians, helping manage workforce shortages while radiologists retain responsibility for diagnosis and treatment decisions. Evidence, not hype, should guide debates about AI and work.

This research develops an onboard AI diagnostic assistant for space missions that can independently investigate life-critical anomalies. By learning how humans ask strategic diagnostic questions, the system combines language models and traditional AI to actively reason through unprecedented spacecraft failures when communication with Earth is delayed.