Nikita Sivakumar

This study uses intravital imaging and single-cell analysis to show that EZH2 mutations alter GC B cell motility and interactions, promoting lymphomagenesis.

The paper develops a computational framework to quantify lymphocyte motility behaviors and predict their impact on B-T interactions in germinal centers, finding that differential motility enhances B-T interaction rates.

The paper develops deep learning models to detect torsional eye movements using video-oculography data, achieving 0.9308 AUROC and 86.79% accuracy.

The paper develops and validates agent-based models to predict multicell patterns driven by signaling-induced differential adhesion and uses machine learning to map parameters to patterns.