• PhD, University of North Carolina at Chapel Hill, 2019, Biostatistics
• BS, Southeast University, 2008, Applied Mathematics

Chao’s research interests mainly focus on statistical learning of large-scale biomedical data including clinical, imaging, and genomic data. His research aims to develop novel statistical methods and machine learning (deep learning) algorithms for analyzing data with complex structures, including high dimensional data, functional data, manifold data, and data with heterogeneity. These statistical methods and computational tools can help us understand the disease progression and improve clinical trials for treatment and early prevention. He is currently working on projects: big data integration, manifold data analysis, functional data analysis, imaging heterogeneity, imaging genetics, causal inference, and deep learning.