We develop mathematically grounded and computationally efficient approaches for multiscale biological systems, integrating applied mathematics, scientific machine learning, and data-driven computation for biomedical discovery and translational research.
Our research integrates scientific computing, machine learning, and data-driven approaches to advance the quantitative understanding of blood flow, red blood cell biomechanics, and cellular dynamics in health and disease. We aim to develop interpretable and predictive frameworks that bridge fundamental science and biomedical applications.
