Research Interests:
- Statistical machine learning for high dimensional data
- Bayesian sparse learning and its applications in cancer genomics
- High/Ultra-high dimensional robust variable selections
- Integrative analysis of cancer genomics data from multiple platforms
- Large scale (convex and non-convex) optimizations in statistical genomics and bioinformatics
- Adaptive Bayesian prediction of patient recruitment in clinical trials
My research is to develop novel and efficient statistical machine learning methods for the integrative analysis of multiple types of cancer genomic data, in order to better elucidate cancer etiology and prognosis.
Research Interests:
- High dimensional data
- Statistical machine learning
- Bayesian sparse learning and its application in cancer genomics
- Statistical genetics and bioinformatics
- Adaptive Bayesian prediction of patient recruitment in clinical trials
Teaching Interests:
- Multivariate statistics
- Statistical computing
- High dimensional data
- Statistical machine learning
In cancer research, profiling studies have been extensively carried out to obtain multiple types of genomic measurements such as mRNA expression levels, copy number variations, DNA methylation and histone modifications, and many others.
Research: Statistics - Studies the integration of multiple types of cancer genomic data to better elucidate cancer etiology and prognosis
