Sanat K. Sarkar

Sanat K. Sarkar

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Cyrus C.K. Curtis Professor in Statistics


Dr. Sanat K. Sarkar is an internationally recognized researcher who has made fundamental contributions to the development of the field of multiple testing toward its applications in modern scientific investigations, such as in genomics and brain imaging.

His research has been funded by the National Science Foundation and the National Security Agency, and often been cited in peer-reviewed journals. He has delivered invited talks at numerous national and international conferences.

He co-organized a major conference on Multiple Comparisons funded by the NSF-CBMS and severed on the organizing committees of several international conferences on the same topic. He has served on the editorials boards of several respectable journals, like the Annals of Statistics, the American Statistician, and Sankhya.

Dr. Sarkar has been recognized as a fellow by both the Institute of Mathematical Statistics and the American Statistical Association, and as an elected member of the International Statistical Institute. He was awarded the Musser Award for excellence in research by the Fox School and inducted several times to the Dean’s Research Honor Roll.

Research Interests

  • Multiple Testing
  • Statistical Methodolgies
  • High-Dimensional Statistical Inference
  • Multivariate Statistics

Courses Taught




STAT 8104

Mathematics for Statistics


STAT 9001

Advanced Statistical Inference I


STAT 9002

Advanced Statistical Inference II


Selected Publications


  • Nandi, S., Sarkar, S.K., & Chen, X. (2021). Adapting to one- and two-way classified structures of hypotheses while controlling the false discovery rate. Journal of Statistical Planning and Inference, 215, 95-108. Elsevier BV. doi: 10.1016/j.jspi.2021.02.006.

  • Sarkar, S., Rom, D., & McTague, J. (2021). Incorporating the sample correlation into the testing of two endpoints in clinical trials. Journal of Biopharmaceutical Statistics, 31(4), 391-402. Informa UK Limited. doi: 10.1080/10543406.2021.1895191.

  • Sarkar, S. & Nandi, S. (2021). On the Development of a Local FDR-Based Approach to Testing Two-Way Classified Hypotheses. Sankhya B, 83. doi: 10.1007/s13571-020-00247-6.

  • Chen, X., Doerge, R., & Sarkar, S.K. (2020). A weighted FDR procedure under discrete and heterogeneous null distributions. Biom J, 62(6), 1544-1563. Germany. 10.1002/bimj.201900216

  • Guo, W. & Sarkar, S. (2020). Adaptive controls of FWER and FDR under block dependence. Journal of Statistical Planning and Inference, 208, 13-24. doi: 10.1016/j.jspi.2018.03.008.

  • Chen, X. & Sarkar, S. (2020). On Benjamini–Hochberg procedure applied to mid p-values. Journal of Statistical Planning and Inference, 205, 34-45. doi: 10.1016/j.jspi.2019.06.001.

  • Sarkar, S.K., Chen, A., He, L., & Guo, W. (2019). Group sequential BH and its adaptive versions controlling the FDR. Journal of Statistical Planning and Inference, 199, 219-235. Elsevier BV. doi: 10.1016/j.jspi.2018.07.001.

  • Lynch, G., Guo, W., Sarkar, S., & Finner, H. (2017). The control of the false discovery rate in fixed sequence multiple testing. Electronic Journal of Statistics, 11(2), 4649-4673. doi: 10.1214/17-EJS1359.