This project developed AI Care, a voice-based caregiving system for people with early-stage Alzheimer's disease. Unlike conventional voice assistants, it uses caregiver-maintained medical records to provide personalised, safety-aware support. By adapting to users rather than requiring users to adapt to technology, AI Care aims to extend safe, independent living at home.

This research applies large language models to decode and design proteins by treating amino acid sequences as biological languages. By identifying hidden structural and functional patterns across massive protein datasets, the work enables creation of novel proteins for medicine, cancer therapy, carbon capture, and environmental remediation beyond naturally evolved biological systems.

Generative AI chatbots are predictive systems that generate human-like responses without true understanding. Using large datasets, they model word relationships similarly to weather forecasting. While effective, they can produce convincing inaccuracies, or “hallucinations.” This research emphasizes interpreting AI realistically—as probabilistic tools with limitations—rather than attributing human cognition to them.

AI can answer religious questions, but it often blends traditions and provides incomplete answers. While specialized models exist, general models like ChatGPT can perform better due to broader training data. The key insight is that theology remains a human, dialogical process—AI should assist, not replace, human judgment and interpretation.

This research develops explainable AI systems to detect early signals of ideological extremism and potential violence in online communications. By integrating social science and machine learning, the project produces interpretable threat assessments for prevention efforts. The framework also extends to healthcare, including rare disease detection using explainable AI models.

This neuroscience study shows that brief pre-lecture interactions significantly improve learning. Students who chatted with either a human teacher or an AI tutor before watching a video lecture performed better and showed greater brain synchrony in MRI scans. Social interaction—human or artificial—primes the brain for more effective learning.

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.