The long-term goal of our lab is to transform human medicine by enabling technologies for in-vivo, directed (re)programming of stem cells into specialized cell types.

In recent years, several experimental methods have been developed to systematically map the epigenetic landscape of individual cells, and quantify the gene expression levels of transcription factors. This allows us to build data-driven models that explain the role of these proteins in cellular decision-making. In our research group, we apply such experimental methods (specifically, single-cell and single-molecule genomics technologies) to understand cellular decision-making. These methods produce large-scale data, typically measuring thousands of genes over thousands of cells, molecules, or samples. We use these datasets to develop statistical and machine learning models that can explain the relationship between the regulators (transcription factors, chromatin state), and a cell’s current or future state. We also develop efficient and easy-to-use bioinformatic software to solve challenges with data pre-processing, visualization and integration.