CMX Lunch Seminar

Recent advances in automated experimental imaging allow for high-resolution tracking of locomotion across biological scales, from whole animal behavior to single-cell trajectories during embryogenesis. Inferring distinct dynamical states from these high-dimensional data and transforming them into effective low-dimensional models is an essential challenge to characterize and compare dynamics within and across species. Spectral mode representations have been used successfully across physics, from quantum mechanics to fluid turbulence, to compress dynamical data. We develop a computational framework that combines geometry-aware spectral mode representations with wavelet analysis and dynamical systems inference to realize a generic procedure for characterizing the dynamics of locomotion. We demonstrate the framework by applying it to tracked centerlines of Pterosperma flagella. Finally, I will discuss ongoing work generalizing this approach to datasets with graphical structures, such as limbed animals.