Hello — I’m Jyotishka.

About

Brief bio: 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.

Quick links

• GitHub
• Google Scholar
• CV (PDF) — Updated September 2025
• jyotishka@vt.edu

Currently under review

• Polynomial Log-Marginals and Tweedie's Formula: When Is Bayes Possible?
• Prediction-powered inference with inverse probability weighting
• Bayesian ICA with super-Gaussian Source Priors
• The Curious Problem of the Normal Inverse Mean: Robustness and Shrinkage
• Inverse Probability Weighting: from Survey Sampling to Evidence Estimation

Selected publications

• Asymptotic Properties of Bayes Risk for the Horseshoe Prior Bayesian Analysis
• Bayesian inference on quasi-sparse count data Biometrika
• Default Bayesian analysis with global-local shrinkage priors Biometrika
• Lasso meets horseshoe Statistical Science
• Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors Bayesian Analysis

Teaching

• STAT 3504: Nonparametric Statistics (Fall 2025)
• CMDA 2006: Integrated Quantitative Sciences - II (Spring 2026)
• Past teaching: STAT 4504/5504G (Applied Multivariate), CMDA 4654 (Intermediate Data Analytics and ML), CMDA 2014 (Data Matter), Stat 5525 (Data Analytics)

Contact

Department of Statistics, Virginia Tech.
Blacksburg, VA.