Jyotishka Datta

Associate Professor

Jyotishka Datta is an Associate Professor of Statistics at Virginia Tech, where he previously served as an Assistant Professor. From 2016 to 2020, he was an Assistant Professor in the Department of Mathematical Sciences at the University of Arkansas, Fayetteville. His research focuses on Bayesian methodology and theory for structured high-dimensional data, including shrinkage estimation, sparse signal recovery, graphical modeling, and nonparametric Bayes. His work spans applications in astronomy, cancer genomics, neuroscience, ecology, and crime forecasting. He received the NSF CAREER Award in 2025 and the Dayanand Naik Award from the Virginia Chapter of the American Statistical Association in 2023.

Professional Preparation

Postdoctoral Fellow. Department of Statistical Science, Duke University and SAMSI (Program: Beyond Bioinformatics.). Mentors: Prof. David B. Dunson (Statistics), and Prof. Sandeep S. Dave (Medicine).
Ph.D. in Statistics, Purdue University. Advisors: Prof. Jayanta K. Ghosh and Prof. Michael Yu Zhu. Thesis title: “Some Theoretical and Methodological Aspects of Multiple Testing, Model Selection and Related Areas” (available here). (August, 2009-May, 2014).
B.Stat and M.Stat: Indian Statistical Institute (2003-2008).

Awards and Honors

NSF CAREER. Heavy-Tailed Priors for Robust Bayesian Inference in Ecology, Machine Learning, and Astronomy NSF-DMS-2443282.
Dayanand Naik Award from the Virginia Chapter of American Statistical Association. From the website: “The Dayanand Naik award recognizes an individual (who works, or resides in Virginia, during the time of the award) for outstanding research contributions and service to the Commonwealth of Virginia in statistics and related fields.“
Robert and Sandra Connor Endowed Faculty Fellowship from the University of Arkansas, 2018-19.
Honorable Mention Award for Best Theoretical Poster at the O’Bayes 2013 Meeting: The Tenth International Workshop on Objective Bayesian Statistics, December 15-19, Durham, Raleigh, USA. Link (p. 8)
William J. Studden Publication Award for an outstanding publication in a mathematical statistics journal, 2013, Department of Statistics, Purdue University.

STAT 3504: Nonparametric Statistics.
STAT 45404/5504G: Multivariate Statistics.
CMDA 2006. Integrated Quantitative Science (Statistics part).
CMDA 2014. Data Matter.
STAT 5525. Data Analytics.
CMDA 4654: Intermediate Data Analytics and ML.

Bayesian methodology and theory, Statistical Computing, Sparse signal recovery, Global-local shrinkage priors, Changepoint detection, Default Bayes, Discrete data, High-dimensional data, Geospatial data, Compositional data. Applications in Cancer Genomics, Epidemiology, Neuroscience, Bioinformatics, Criminology and Ecology.

See my Google Scholar profile for a complete and up-to-date list:
https://scholar.google.com/citations?user=_wA03WkAAAAJ&hl=en

Five selected papers:

Datta, J., and Ghosh, J. K. (2013), “Asymptotic Properties of Bayes Risk for the Horseshoe Prior". Bayesian Analysis 8(1), 111-132.
Datta, J. and Dunson, D. B. (2016), “Bayesian inference on quasi-sparse count data", Biometrika, 103 (4): 971-983.
Bhadra, A., Datta, J*., Polson, N. G., & Willard, B. T (2016), (*alphabetical) “Default Bayesian analysis with global-local shrinkage priors", Biometrika, 103 (4): 955-969.
Bhadra, A., Datta, J.∗, Polson, N. G., & Willard, B. T (2019), (*alphabetical), “Lasso Meets Horseshoe – A Survey". Statistical Science, 34(3), 405-427
Boss, J., Datta, J., Wang, X., Park, S., Kang, J., Mukherjee, B. (2023), “Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Block-Correlated Predictors". Bayesian Analysis, 1(1), 1-30.