The tempo and mode of tumor evolution through space and time

We leverage multi-region, longitudinal, and/or single-cell genomic data combined with agent-based computational model to reveal the quantitative principles for the evolutionary dynamics of tumor growth and metastasis.

The evolutionary patterns of cancer subpopulations under immune selection

We integrate multi-region sequencing, single cell transcriptomics and computational models to understand how cancer cells evolve to escape our immune surveillance and how immune selection drive the evolutionary trajectories of cancer cells.

The clonal dynamics of stem cells during tissue development and renewal

We combine lineage tracing, single-cell transcriptomics and mathematical models to study the clonal dynamics of stem cells during tissue development and renewal.

Computational methods for analyzing single-cell lineage tracing data

We develop computational methods for analyzing simultaneous single-cell transcriptomic and lineage tracing data, with an aim to understand the cell-state transitions in organ development and tumor evolution.