Biography
Sanat K. Sarkar is the Cyrus C. K. Curtis Professor of Statistics in the Department of Statistics, Operations, and Data Science at the Fox School of Business, Temple University. He earned his B.Sc. in Statistics from Presidency College (now University) in India, and his M.Sc. and Ph.D. in Statistics from Calcutta University. While completing his Ph.D., he served as a Lecturer in the Department of Statistics at Calcutta University. He subsequently joined the Department of Mathematics and Statistics at the University of Pittsburgh as a Research Associate before joining Temple University in 1982, where he has been a faculty member and served as Department Chair from 2012 to 2022.
Sarkar is an internationally recognized researcher who has made foundational contributions to the development of multiple testing, significantly advancing its applications in modern scientific investigations. Notably, he is known for providing a proof of Simes’ conjecture. His work has been supported by numerous National Science Foundation grants, several as Principal Investigator, and published in leading statistical journals, including Annals of Statistics, Journal of the American Statistical Association, and Biometrika. He has delivered over 80 invited lectures worldwide, including plenary talks and named lectures, at major national and international conferences and academic institutions.
A Fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an Elected Member of the International Statistical Institute, Sarkar has received multiple awards for research excellence, including the Musser Award for Leadership in Research, as well as repeated recognition for top-tier publications and outstanding performance in sponsored projects at the Fox School.
He has provided extensive service to the statistical community, serving as Associate Editor for Annals of Statistics, The American Statistician, and Sankhyā: Series B, as well as on review panels for journals and funding agencies worldwide. He co-organized a major NSF-CBMS-funded conference on Multiple Comparison Procedures and has served on the organizing committees of several international conferences on the same topic, most recently leading the 13th International Conference on Multiple Comparison Procedures (MCP 2025) in Philadelphia. He has also organized and chaired sessions at numerous other international conferences.
At Temple University, Sarkar has taught a wide range of undergraduate and graduate courses, supervised more than 30 doctoral students as primary advisor, and mentored many others through doctoral advisory committees and independent studies.
In addition to his academic work, he has served as a statistical consultant and advisory board member for pharmaceutical companies and research organizations, applying his expertise to complex problems in medicine, biology, and industry, and helping bridge the gap between theory and practice
Research Interests
- Multiple Testing
- Statistical Methodolgies
- High-Dimensional Statistical Inference
- Multivariate Statistics
Courses Taught
Number | Name | Level |
|---|---|---|
STAT 8104 | Mathematics for Statistics | Graduate |
STAT 9001 | Advanced Statistical Inference I | Graduate |
STAT 9002 | Advanced Statistical Inference II | Graduate |
Selected Publications
Recent
Sarkar, S.K. & Zhang, S. (2025). Shifted BH methods for controlling false discovery rate in multiple testing of the means of correlated normals against two-sided alternatives. Journal of Statistical Planning and Inference, 236, 106238-106238. Elsevier BV. doi: 10.1016/j.jspi.2024.106238.
Sarkar, S.K. (2025). Comments on “Data Fission: Splitting a Single Data Point”. Journal of the American Statistical Association, 120(549), 170-171. Informa UK Limited. doi: 10.1080/01621459.2024.2414865.
Nandi, S. & Sarkar, S.K. (2024). Further results on controlling the false discovery rate under some complex grouping structure of hypotheses. Journal of Statistical Planning and Inference, 229, 106094-106094. Elsevier BV. doi: 10.1016/j.jspi.2023.07.008.
Sarkar, S.K. & Tang, C.Y. (2022). Adjusting the Benjamini–Hochberg method for controlling the false discovery rate in knockoff-assisted variable selection. Biometrika, 109(4), 1149-1155. Oxford University Press (OUP). doi: 10.1093/biomet/asab066.
Sarkar, S.K. & Zhao, Z. (2022). Local false discovery rate based methods for multiple testing of one-way classified hypotheses. Electronic Journal of Statistics, 16(2). Institute of Mathematical Statistics. doi: 10.1214/22-ejs2080.
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.