Jingjing  Yang

After completing my Ph.D. in statistics at Rice University, I have been studying Statistical Genetics since my Postdoctoral training at the Biostatistics Department of the University of Michigan at Ann Arbor. I am interested in developing statistical methods and tools to integrate multi-omics data for studying complex human diseases, such as Alzheimer's Disease, mobility disability, and Parkinson's disease. We utilize Bayesian statistical methods, machine learning, and deep learning methods with efficient computational algorithms for studying multi-omics data. My lab has also been working on analyzing next-generation sequence data (bulk seq and single nucleus seq) and biomedical signal/image data. The programming languages we use include R, Python, C++, and Perl. The computation environments we use are Linux/Unix computation clusters with shell/bash scripts and Amazon Web Services. The current research topics include integrating molecular genomics data and functional annotations with GWAS summary data for mapping risk genes of complex diseases, deriving risk prediction models for Alzheimer's disease, and sequencing data analysis.