This research seeks blood-based biomarkers that predict which people infected with Chagas disease will later develop life-threatening cardiomyopathy. By analysing immune proteins in blood samples from Bolivia, it aims to enable earlier diagnosis, targeted monitoring, and preventative treatment, offering a model for predicting and preventing many chronic diseases before irreversible damage occurs.

This research investigates how Pseudomonas aeruginosa adapts to drinking water systems before causing human infections. By identifying a previously unknown gene essential for biofilm formation and survival, the work provides new insight into how dangerous bacteria prepare for infection and reveals potential targets for preventing disease before it develops.

This research investigates how lung mucus and its mucin molecules defend against Coccidioides, the fungus that causes Valley fever. By showing that mucins slow fungal growth, the work suggests mucus shapes infection before symptoms appear, opening new possibilities for earlier diagnosis and treatments against Valley fever and other infectious diseases.

This research develops targeted lipid nanoparticle delivery systems to improve tuberculosis treatment and vaccination. By replacing PEG coatings and using mannose to target infected macrophages, it aims to deliver drugs more effectively, reduce treatment duration, improve vaccine performance, and contribute to the global elimination of tuberculosis.

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 research uses a high-throughput screening platform called EpiScan to identify HIV peptides that bind strongly to MHC molecules and appear on infected cell surfaces. By discovering these immune-visible targets, the work aims to improve detection and elimination of hidden HIV reservoirs, supporting the development of future HIV therapies.

This research uses wastewater-based epidemiology to monitor antibodies excreted by communities, providing early insights into population vulnerability to infectious diseases. By analyzing antibody trends in wastewater over time, the work helps public health authorities identify at-risk communities, allocate resources more effectively, strengthen vaccination strategies, and improve outbreak preparedness.

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 investigates tularemia, a highly infectious disease caused by Francisella tularensis, and explores a weakened bacterial strain as a vaccine candidate. By studying how the pathogen evades immune defenses, the work aims to enable rapid immune recognition and response, improving protection against both natural infections and potential biothreats.

This research explores how mast cells—immune cells responsible for allergy symptoms—can be repurposed to strengthen vaccines. By targeting mast cells with nasal vaccines, stronger and longer-lasting immune responses may be generated, particularly benefiting high-risk populations and improving protection against infectious diseases.