1. Statistics: We are particularly interested in developing statistics methods and theories for high dimensional data and big data motivated by the problems coming from biological and medical researches. Currently, we focus on high dimensional methods for network inference and clustering analysis. We also work on Bayesian as well as big data methods.
2. Bioinformatics: We develop statistics and machine learning methods for high-throughput sequencing data. A number of our tools for whole genome sequencing data are widely used. We recently focus on developing computational methods for single cell data and third generation sequencing data.
3. Tumor omics study and precision medicine: We analyze large-scale tumor omics data to reveal important molecular patterns of tumor and its microenvironments. We also build statistics or machine learning models that are predictive for patient’s prognostics or responses to cancer treatments.
If you are interested in our researches and would like to do research with us, you are encouraged to send us an email with your CV attached. All levels of students (undergraduate, graduate and post-docs) are welcomed to join us. If you do not receive a reply in a week, feel free to send another reminding email.