Project with Lin Zhang

Statistical and Machine Learning Methods for Large-Scale Single-Cell Omics Data

Single-cell omics data is vital to our understanding to cell-to-cell heterogeneity and cell differentiation. Statistical and machine learning methods have played a central role in unveiling the biological heterogeneity in single cells. In this project, we will explore statistical and machine learning methods to jointly analyze single-cell omics data generated from heterogeneous sources. We will further generalize these methods to efficiently analyze atlas-level datasets with multi-million single cells.