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.

Antibiotic resistance threatens to return medicine to a pre-antibiotic era. This research uses machine learning to study how bacteria balance resistance to antibiotics and bacteriophages. By revealing genetic trade-offs between attack and defense, the work enables smarter combination therapies that exploit bacterial weaknesses and prevent otherwise deadly infections.

Mel-AI is an artificial intelligence system designed to assist pathologists in distinguishing melanoma from benign moles. By training computer-vision models on 520 cases, the system reached 96% accuracy and interpretable outputs. It offers scalable, objective quality assurance, reducing misdiagnosis risk and improving melanoma detection in high-incidence countries like Australia.