Abstract
"The rapid development of genomics technologies has propelled fast advances in genomics data science. While new computational algorithms have been continuously developed to address cutting-edge biomedical questions, a critical but largely overlooked aspect is the statistical rigor. In this talk, I will introduce our recent work that aims to enhance the statistical rigor by addressing three issues: 1. large-scale feature screening (i.e., enrichment and differential analysis of high-throughput data) relying on ill-posed p-values; 2. double-dipping (i.e., statistical inference on biasedly altered data); 3. gaps between black-box generative models and statistical inference."