Jiashun Jin received his Ph.D in Statistics from Stanford University in 2003. He was trained in statistical inference for Big Data, specialized in dealing with the most challenging regime where the signals are so rare and weak that many conventional approaches fail, and it is desirable to find new methods and theory that are appropriate for such a situation. His earlier work was on large-scale multiple testing, focusing on the development of (Tukey’s) Higher Criticism and practical False Discovery Rate (FDR) controlling methods. His subsequent research has been on the interdisciplinary area of statistical decision theory and machine learning, where he found new use of Higher Criticism on the problems of cancer classification and clustering with sparse PCA. More recently, he has developed a class of methods called graphical screening for high dimensional variable selection. His most recent interest is on complicate network data and random matrix theory. Jin received NSF CAREER award in 2007, IMS Tweedie Award in 2009, and was elected IMS Fellow in 2011. He has served as organizational co-chair of the second IMS China Conference in 2009.