Xinyu Zhou

The paper develops a machine learning model to predict SARS-CoV-2 mutation trends using historical data, achieving low prediction error and robustness across mutation patterns.

S3R models spatially varying associations in spatial transcriptomics data by estimating location-specific coefficients with structured sparsity and smoothness penalties, showing effectiveness in synthetic and real datasets.