For a complete list of publications,
see his Google Scholar.
For a complete list of developed software,
see his GitHub.
For more details about Chao's background, please read his CV.
07/19 Chao finished his thesis
"Advanced statistical learning methods for heterogeneous medical imaging data"
(advisor: Dr. Hongtu Zhu)
Chao's research interests mainly focus on statistical learning of large-scale biomedical data including clinical,
imaging, and genomic data.
The goal of his research is to develop novel statistical methods and machine learning (deep learning) algorithms
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 the
treatment and early prevention. Some projects that he is currently working on are: big data integration,
manifold data analysis, functional data analysis, imaging heterogeneity,
imaging genetics , and deep learning. Chao is also interested in some public datasets including
Alzheimer’s Disease Neuroimaging Initiative [ADNI],
The Osteoarthritis Initiative [OAI],
UK Biobank [UKB],
Adolescent Brain Cognitive Development [ABCD], and
Human Connectome Project [HCP].