We develop computational and statistical solutions for the analysis of high-dimensional molecular data in various contexts of biomedical research. These tools are motivated by a wide range of research topics, including

• Network inference and interpretable integration of multi-omics data in biological experiments and clinical studies

• Development of novel informatics tools for large-scale untargeted and targeted mass spectrometry (MS) data processing in metabolomics

• Effect size-driven analysis approaches for protein modifications, interactions, and other applications of proteomics

• Using Atlas data sets as a prior information to make cell population-level inference from bulk -omics data

• Clinical application of sequencing and mass spectrometry-based omics technologies

To see examples of our work, check out the CSSB LAB GitHub page!