Our primary research interest is to develop computational methods for analyzing modern “omics” data, especially for single-cell and spatial transcriptomics. Our research combines statistical modeling, bioinformatics, and machine learning to provide a more rigorous interpretation of biological signals. Some specific topics include:

1. Generation of realistic in silico single-cell and spatial multi-omics data;


2. Computational modeling of gene co-expression in single-cell and spatial transcriptomics;


3. Detection of differential expression in single-cell and spatial transcriptomic;


4. Gene selection, dimensionality reduction, and cell subsampling for large-scale datasets.