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The value of computational modeling in precision cancer healthcare is difficult to overstate. Emerging developments in cancer assessment techniques and therapeutics provide tons of opportunities to detect cancers earlier, monitor response more accurately and manage cancers with better outcomes. Along with the increasing opportunities, however, decision-making to find interpretable diagnostic and prognostic markers or to plan the optimal treatment for individual patients becomes increasingly flexible and complex. Without computational approaches powered by both mechanism-informed modeling and statistical analysis, it would be extremely challenging to interpret essential information from high-dimensional data such as imaging, nor to systematically evaluate various therapeutic options.

Thereby, my research interests focus on integrating advanced medical imaging techniques, image analysis approaches and computational modeling to hasten the arrival of personalized cancer healthcare. In particular, my current research themes are:

1. Developing image-guided mathematical models to represent the mechanisms of tumor growth, tumor-associated vasculature and microenvironment, systemic drug delivery and therapy-induced responses.
2. Investigating numerical algorithms to enable efficient model implementation for response prediction and optimization with a quantified uncertainty.
3. Developing longitudinal image processing, multi-modal data integration and statistical approaches to improve cancer early detection and characterization.