Statistical and Computational Methods

Developing AI-driven analytical tools for digital pathology imaging data

Developing AI-driven analytical tools for digital pathology imaging data

The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs), which are invaluable for examining cellular morphology and its changes during embryonic development or disease progression. Many existing methods utilize well-trained deep neural networks to extract image features from histology images, which are then used for downstream analysis. [...]
Developing statistical tools for single-cell RNA sequencing analysis

Developing statistical tools for single-cell RNA sequencing analysis

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of disease molecular mechanisms. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, [...]
Developing machine learning methods for spatial multi-omics integration

Developing machine learning methods for spatial multi-omics integration

Multi-modal spatial omics represents a paradigm shift in understanding complex biological systems by integrating diverse omics modalities within their native tissue contexts. Different spatial omics techniques capture distinct molecular information, including transcriptomics, proteomics, metabolomics, chromatin accessibility, and histone modifications. Together, these modalities provide a comprehensive view of cellular and tissue functions, which is crucial for [...]