YSPH Biostatistics Seminar: “Opening Black Boxes: Enhancing Statistical Rigor in Genomics Data Science”

NOTE: BIS 525 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

SPEAKER: Jingyi Jessica Li, PhD, Associate Professor, Department of Statistics, University of California, Los Angeles

TITLE: "Opening Black Boxes:Enhancing Statistical Rigor in Genomics Data Science”

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 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.