School of Public Health
These researchers develop and evaluate various statistical and computational methods for genome-wide association studies (GWASs), next-generation sequencing data, multiple types of -omic data, and neuroimaging data. They conduct frequent and wide-ranging studies with large-scale simulated and real genetic and -omic data, which require both CPU-intensive computing and a large disk space to store big data. More recently the researchers have expanded their study into neuroimaging involving large-scale MRI data. For example, they would like to develop and evaluate new computational methods to construct functional networks or connectomes based on resting-state functional MRI (fMRI) data. One of their main goals is to integrate multiple types and multiple sources of -omic and neuroimaging data, for which they will continue downloading huge datasets.