Brief Biography:
Jiashun Jin, Professor, received his Ph.D. in Statistics from Stanford University in 2003. His research has focused on statistical inference for big data, especially on the most challenging regime in which signals are both rare and weak.
His earlier work centered on large-scale multiple testing, with a particular focus on Higher Criticism and practical methods for controlling the False Discovery Rate (FDR). He further developed Higher Criticism into a broad class of methods with important applications in genetics, genomics, cosmology, and astronomy, including cancer classification, cancer clustering, and the detection of non-Gaussian signatures in the cosmic microwave background (CMB).
His more recent research interests include complex networks, social networks, sparse PCA, and random matrix theory. He has developed a number of influential methods, including Graphlet Screening (GS) for high-dimensional variable selection, IF-PCA for dimension reduction and high-dimensional clustering, and SCORE for network community detection.
Together with his coauthors, Professor Jin also developed a large-scale dataset on the coauthorship and citation networks of statisticians. The dataset contains bibliographic and citation information for approximately 83,331 papers published in 36 journals in statistics and related fields over 41 years. It has provided a valuable resource for research on statisticians’ collaboration and citation networks, and has also opened new possibilities for the quantitative evaluation of the impact of statistical research.
Honors & Awards:
1. ICCM Distinguished Paper Award (2020)
2. Elected ASA Fellow (2019)
3. IMS AOAS Lecture (2016)
4. IMS Medallion Lecture (2015)
5. Elected IMS Fellow (2011)
6. IMS Tweedie New Researcher Award (2009)
7. NSF CAREER Award (2006)
Selected Papers:
Select Recent Papers:
1. Jin, J., Ke, Z. T., Luo, S., & Ma, Y. (2025). Optimal Network Pairwise Comparison.Journal of the American Statistical Association, 120, 1048–1062.
2. Jin, J., Ke, Z. T., & Luo, S. (2024). Mixed-membership Estimation for Social Networks.Journal of Econometrics, 239.
3. Zhang, H., Liu, M., Jin, J., & Wu, Z. (2023). Signal-noise Ratio of Genetic Associations and Statistical Power of SNP-set Tests.The Annals of Applied Statistics, 17(3), 2410–2431.
4. Jin, J., Ke, Z. T., Luo, S., & Wang, M. (2022). Optimal Estimation of the Number of Network Communities.Journal of the American Statistical Association.
5. Ji, P., Jin, J., Ke, Z. T., & Li, W. (2022). Co-citation and Coauthor Networks of Statisticians.Journal of Business & Economic Statistics, 40(2), 469–485.
6. Jin, J., Ke, Z. T., & Luo, S. (2021). Optimal Adaptivity of Signed-Polygon Statistics for Network Testing.The Annals of Statistics, 49(6), 3408–3433.
7. Jin, J., Ke, Z. T., & Wang, W. (2017). Phase Transitions for High-Dimensional Clustering and Related Problems.The Annals of Statistics, 45(5), 2151–2189.
8. Jin, J., & Wang, W. (2016). Important Features PCA for High-Dimensional Clustering.The Annals of Statistics, 44(6), 2323–2359.
9. Ji, P., & Jin, J. (2016). Coauthorship and Citation Networks for Statisticians.The Annals of Applied Statistics, 10(4), 1779–1812.
