Xinzhou Ge, Postdoctoral Fellow
Department of Statistics, UCLA
Location: Young Hall CS50
Large-scale feature screening is ubiquitous in high-throughput biological data analysis: identifying the features (e.g., genes, mRNA transcripts, and proteins) that differ between conditions from numerous features measured simultaneously. The false discovery rate (FDR) is the most widely-used criterion to ensure the reliability of screened features. The most famous Benjamini-Hochberg procedure for FDR control requires valid high-resolution p-values, which are, however, often hardly achievable because of the reliance on reasonable distributional assumptions or large sample sizes. Motivated by the Barber-Candes procedure, Clipper is a general statistical framework for large-scale feature screening with theoretical FDR control and without p-value requirement. Extensive numerical studies have verified that Clipper is a versatile and effective tool for correcting the FDR inflation crisis in multiple bioinformatics applications.
Xinzhou obtained his Ph.D. degree in 2021 from Department of Statistics at UCLA, where he worked with Prof. Jingyi Jessica Li. He received his Bachelor of Statistics from School of Mathematical Science, Peking University in 2016. After graduating from UCLA, Xinzhou continued working with Prof. Jingyi Jessica Li as a postdoc.