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.
Funding
PI. (with Co-PI W. Wang). Integrative Lipid - Environment Analysis for Cancer Prevention Studies. Innovative Research Award. Johnson Cancer Research Center. 2017 - 2018.
Travel award for the annual Arthur M. Sackler Colloquium at the National Academy of Sciences. 2017.
PI. Robust Network Analysis of High Dimensional Genomics Data for Cancer Prognosis. K State Faculty Enhancement Award. 2017 - 2018.
PI. Summer workshop on Statistical Machine Learning for High-dimensional Data. 2018. Co-sponsored by Shell Oil.
Co-PI. HPV Oncogenes Dysregulate Translesion Synthesis. 05/2020 - 04/2023. NIH R15.
PI. (with Co-PI W. Wang). Bayesian Analysis of Longitudinal Lipidomics Data in Cancer Prevention Studies. Innovative Research Award. Johnson Cancer Research Center. 12/2021 - 05/2023.