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Our lab focuses on studying cell dynamics in various biological processes in many diseases (e.g., developmental disorder, pulmonary diseases, cancers). Decoding cell dynamics is essential for understanding the pathogenesis of diseases and finding novel therapeutics. The existence of enormous heterogeneity in those diseases makes it challenging to decipher the unknown.

The advancing single-cell technologies that profile individual cell states provide unprecedented opportunities to tackle this problem, which could drive biological discoveries and medical innovations in various fields (such as developmental and cancer biology). However, the single-cell data presents numerous new challenges in developing computational models that bridge the biomedical data and potential discoveries.

Our primary research is to develop machine learning approaches (particularly probabilistic graphical models) to jointly analyze, model, and visualize single-cell (and/or bulk) omics data (preferably longitudinal or spatial). Such computational models will be used to help us derive a deeper understanding of the cell dynamics in different biological systems, which will eventually benefit the public health with machine-learning driven new diagnostic and therapeutic strategies.