Imaging

4D Holographic Particle Tracking Velocimetry (PTV) (Feb 2026 - Present) Undergraduate Researcher | Computational Imaging Lab, UC Berkeley Advised by Dr. Nalini Singh & Mingxuan Cai Details Lagrangian Representation: Departed from traditional grid-based views by adopting a Lagrangian perspective, using precise floating-point coordinates to represent individual particles for off-grid trajectory tracking. Forward Modeling: Developed an analytical forward projection model to optimize optical kernels, improving the fidelity of in-line holographic reconstructions. Hybrid Initialization: Implemented an Angular Spectrum Method (ASM) combined with Gaussian representations for robust particle localization. Physics-Informed Neural Field: Represented complex velocity fields using Multi-Layer Perceptrons (MLPs) to regularize ill-posed inverse problems in fluid dynamics. Pipeline Engineering: Built an end-to-end pipeline integrating particle localization and 4D flow field reconstruction. Zero-Day Vulnerability Detection via Revelio (Mar 2026 - Present) Research Assistant | Sky Computing Lab, UC Berkeley Advised by Yiwei Hou ...

Course Overview An intensive exploration of deep learning applications in medical imaging. The coursework involved implementing and optimizing advanced neural network architectures for dynamic MRI reconstruction and cardiac image segmentation, balancing computational efficiency with high-fidelity clinical outputs. Core Project 1: Dynamic MRI Reconstruction Engineered a deep learning pipeline for reconstructing dynamic cardiac cine MRI from 5x undersampled k-space data, addressing the trade-off between spatial-temporal resolution and acquisition time. ...