This research applies fluid mechanics, numerical simulations, and machine learning to model the brain’s waste-clearance system during sleep. By investigating how fluid moves through brain tissue and how aging or injury affect this process, the work aims to identify strategies for preventing or slowing neurodegenerative diseases such as Alzheimer's.

This research uses artificial intelligence to predict the progression of Alzheimer’s disease and cancer using medical imaging data. By analyzing brain scans, tumor scans, and treatment responses, AI models can forecast disease development and treatment outcomes, enabling earlier intervention, more personalized care, and improved quality of life for aging populations.

Flash memory stores essential data but degrades with repeated use, limiting reliability in long-term applications like cars and satellites. Inspired by biological circadian rhythms, this research introduces “recovery periods” for memory cells to rest and repair. The approach improves flash memory lifespan up to ninefold, enabling more durable and dependable storage systems.

This research develops one of the most advanced human-engineered brain models to better study Alzheimer’s disease and test treatments. Using microfluidic chips containing all key brain cell types, blood-vessel systems, and Alzheimer’s-model neurons, the project enables efficient drug testing, personalised disease modelling, and the possibility of replacing animal testing in the search for a cure.