Active Matter: Active Matter refers broadly to collectives of physical and biological systems, ranging from groups of migrating cells and swarms of birds to animals that convert internal energy into active motion at the individual level. We are interested in understanding the role of deformability, intrinsic heterogeneity, and surface adhesion in biological collective behavior using multi-scale simulations of active particles.

Population Dynamics: Microbial population dynamics is dictated by single-cell growth, which is determined by underlying genetic networks and cell-cell physical interactions. We aim to develop a multi-scale simulation framework that integrates the dynamics of gene regulatory networks and cell mechanics, driven by physical collisions, to study the surface morphology and spatial expansion of bacterial colonies formed by different types of bacteria.

AI & Data Science: Recent advances in experimental and high-resolution microscopy have provided us with unprecedented details of cell shape, mechanics, and cell-cell interaction mechanisms during various biological processes. However, interpreting such large volumes of data requires the development of data analysis tools that can characterize the data into low-dimensional measures as well as help us develop better theoretical models. We are particularly interested in developing novel algorithms for motion and shape tracking, spatial clustering, image correlations, and distance measurements to characterize experimental datasets.