My research uses artificial intelligence to detect water pollution by analysing DNA traces left by aquatic species. Instead of relying on visual signs or costly expert identification, supervised machine learning reads species patterns to determine water quality. The method is faster, cheaper, and more accurate than traditional analysis.

My research tackles PFAS (polyfluoroalkyl substances) or “forever chemicals,” found in everyday products and linked to serious health risks. Blood testing shows 95% contamination rates. The project identifies specialised bacteria capable of breaking PFAS down nearly completely within days, offering a promising biological solution to reduce environmental and human exposure to these persistent toxic chemicals.

Electricity access doesn’t always translate into real development benefits. In Timor-Leste, “100% access” still leaves hospitals dark at night. A review of global evidence shows that over a third of electrification outcomes are neutral or negative. This research explores barriers that limit electricity’s impact to inform better policy and community support.

This research investigates whether ballet training can reduce fall risk in older adults. Comparing dancers to non-dancers during unexpected slips, ballet-trained participants showed better stability, faster muscle responses, and fewer falls. The project aims to develop a validated ballet-based fall-prevention syllabus that could significantly improve independence and reduce healthcare costs.