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 develops a machine-learning and data-assimilation framework that combines idealized and operational Earth systems models into a high-resolution, physically realistic “bridging model.” Applied to the El Niño–Southern Oscillation, the approach improves climate simulation accuracy while enabling exploration of alternative climate regimes and physically consistent what-if scenarios.

This research investigates why supersonic aircraft engines fail under turbulent atmospheric conditions. Using high-performance supercomputer simulations, the study models airflow disruptions around supersonic engines to identify early warning signs of instability. The work aims to improve engine reliability and help revive safe, efficient supersonic passenger air travel.