Jiashun Jin received his Ph.D in Statistics from Stanford University in 2003. He was trained in statistical inference for Big Data, specializing in dealing with the most challenging regime where the signals are both Rare and Weak. In such Rare/Weak settings, many conventional approaches fail, and it is desirable to find new methods and theory that are appropriate for such situations.
His earlier work was on large-scale multiple testing, focusing on (Tukey's) Higher Criticism and practical False Discovery Rate (FDR) controlling methods. He has developed the idea of Higher Criticism into a class of methods that are useful for solving problems in genetics and genomics and cosmology and astronomy, including cancer classification, cancer clustering, and nonGaussian signature detection in the Cosmic Microwave Background (CMB). He has proposed to use the so-called ``phase diagram" as a new optimality measure that is particularly appropriate for Big Data settings where the signals of interest are Rare/Weak, and worked out the phase diagrams for many seemingly unrelated settings.
His more recent interest is on complex graphs, social networks, and sparse PCA and Random Matrix Theory. He has developed a number of new methods, among which are the Graphlet Screening (GS) for high dimensional variable selection, IF-PCA for dimension reduction and high dimensional clustering, and SCORE for network community detection.
Jin and coauthors have collected and cleaned a data set for the coauthorship and citation networks for statisticians. The data set consists of titles, authors, keywords, abstracts, and citation counts of approximately 83,381 papers published in 36 journals in statistics and related fields, spanning 41 years. The data set provides a fertile ground for researches in social network of statisticians. It also opens doors for quantitative evaluation of the impacts of statistical research.
