Laboratory - Research

We lead interdisciplinary research at the intersection of computational science, applied mathematics, and experimental oncology. Our portfolio integrates high-performance computing, stochastic and multi-scale modeling, and advanced image analysis to investigate key challenges in tumor evolution, treatment resistance, and the immune microenvironment, with a strong emphasis on translational relevance.

Applied Mathematics in Oncology

Central to our research is the application of mathematical modeling to cancer biology. Using differential equations, stochastic processes, and statistical frameworks, we create quantitative models of tumor growth, immune responses, and radiation effects. These models integrate imaging data, molecular information, and experimental observations to characterize tumor dynamics, predict therapeutic outcomes, and assess the impact of various treatments.

Computational Radiation Biology

We bridge computational techniques with experimental mouse models to explore mechanisms of resistance to radiation. Through the integration of radiomics, pathomics, and systems biology approaches, we study how radiation influences metabolic response, immune cell infiltration, and tissue microenvironment.

High-throughput Image Phenotyping

We leverage radiomics and pathomics techniques to extract large-scale, quantitative features from radiology and pathology images, enabling multi-scale phenotyping of tumors. This high-throughput approach allows us to decode spatial patterns associated with tumor biology, treatment response, and microenvironmental context, supporting both mechanistic modeling and translational biomarker discovery.

Tumor Immunology and Immune Dysregulation

We use next generation single-cell digital spatial profiling and advanced computational techniques to analyze immune cell distributions within tumors, helping to map the complex interactions between tumor cells and immune cells. By integrating tissue-specific data, we seek to understand the role of the immune microenvironment in tumor progression, therapy resistance, and immune evasion.

Digital Twins of Cancer

We develop mechanistically-informed and data-calibrated digital twins as virtual representations of tumors and their microenvironment. Grounded in applied mathematics and informed by imaging, pathology, and molecular data from preclinical and clinical sources, these models serve as a data-driven simulation framework for mechanistic research, drug screening, and biomarker discovery.

radiology.duke.edu | radonc.duke.edu | ece.duke.edu | medicalphysics.duke.edu

Page updated

Google Sites

Report abuse