Overview
I am a Postdoctoral Research Associate in the Department of Biostatistics and Bioinformatics at Duke University. My current research in computational immunology focuses on developing advanced machine learning models, including deep neural networks and multiscale modeling platforms, to design predictive models that enhance vaccine efficacy evaluations and provide deeper insights into maternal-fetal immune interactions. This work integrates stochastic processes, ODEs, and agent-based models to deliver high-performance simulations and predictive analytics, leveraging large-scale biomedical data to drive accuracy and robustness in translational medicine applications.
Previously, as a National Science Foundation Research Fellow in Digital Health (BridgesDH Fellowship) at the WVU Cancer Institute, I had the privilege of being mentored by Professor David Klinke. In the Klinke lab, we developed data-driven methodologies to infer causal dynamic networks between various cell types and a protein secreted by malignant cells (CCN4). This work focused on understanding how these interactions influence the tissue microenvironment by altering cell composition and functionality during the oncogenesis of human breast cancer. My efforts also included developing deep learning and GAN-based computational models to decode tumor heterogeneity and characterize immune profiles, leveraging single-cell data for improved accuracy and bias mitigation.