Statistics and Biological Data Science
Research Directions
We are a group of people interested in developing novel statistics/machine learning methods and using statistics/machine learning techniques to address biomedical problems. We view our ourselves as interdisciplinary researchers and mainly work on cutting edge interdisciplinary problems between computational and biological sciences. We have a diversity of academic backgrounds such as statistics, machine learning, bioinformatics, genetics, molecular biology and cancer biology. We believe that having people with various backgrounds can help us spark new ideas and promote interdisciplinary researches. Our recent research directions are listed below.

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.

How To Join Us

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.