Research

This work introduces a training-free differentiable rendering approach to spline refinement, achieving both high reliability and sub-pixel accuracy. It serves as a drop-in replacement for the popular active contour model.

This work generalizes the hydrodynamic graph model, allowing for additional optimization of node positioning. It results in organic networks that adapt to boundary irregularities and node misalignment while improving overall efficiency.

Using a theoretical model, this study explores how receptors cluster in high-curvature membrane regions, optimizing spatial gradient sensing and aligning with real-cell observations.

A minimal model that provides a quantitative understanding of how cells use pseudopod splitting to achieve high-performance chemotaxis with minimal regulation.

This study develops a stochastic-thermodynamic framework to analyze the relationship between irreversibility and dynamical behavior in high-dimensional chaotic systems.

This study finds that the transition between chemotaxis strategies is continuous and that a combined strategy outperforms constrained variants and explicit spatial-temporal integration models.

An end-to-end deep learning approach for extracting precise shape trajectories of motile, overlapping slender bodies, applied to dense experiments of swimming nematodes.

This thesis explores the behavior of strongly coupled ions at liquid-gas interfaces, providing insights for time projection chambers in physics experiments.