Research
My research focuses on methodological, computational, and theoretical aspects of modern Bayesian statistics & probabilistic machine learning. I greatly enjoy the interdisciplinary nature of statistics and the opportunity to interact and collaborate with domain experts, and simultaneously strive to identify challenging foundational questions stemming from interesting applied problems and provide rigorous mathematical solutions. Some representative publications are listed below, grouped by areas of interest. See my google scholar profile here for the most updated list of publications.
Bayesian high-dimensional shrinkage priors: theory and methods
S. Zhou, P. Ray, D. Pati, and A. Bhattacharya. Under revision.
M. Shin, A. Bhattacharya, and V. E. Johnson. Journal of the American Statistical Association (2020)
A. Chakraborty, A. Bhattacharya, and B. K. Mallick. Biometrika (2020)
A. Bhattacharya, D. Pati, N.S. Pillai, and D. B. Dunson. Journal of the American Statistical Association (2014)
D. Pati, A. Bhattacharya, N.S. Pillai, and D. B. Dunson. The Annals of Statistics (2014)
A. Bhattacharya and D. B. Dunson. Biometrika (2011)
Scalable MCMC for shrinkage priors
1. MCMC for global-local shrinkage priors in high-dimensional settings
A. Bhattacharya and J. E. Johndrow. Handbook of Bayesian Variable Selection (2022). Marina Vannucci and Mahlet Tadesse Eds.
2. Sampling local scale parameters in high dimensional regression models
A. Bhattacharya and J. E. Johndrow. Handbook of Computational Statistics and Data Science (2021). Thomas Lee Eds.
3. Coupled Markov chain Monte Carlo for high-dimensional regression with Half-t priors
N. Biswas, A. Bhattacharya, P.E. Jacob, and J.E. Johndrow. The Journal of the Royal Statistical Society (Series B) (Accepted for publication)
4. Scalable Approximate MCMC Algorithms for the Horseshoe Prior
J. E. Johndow, P. Orenstein, and A. Bhattacharya. Journal of Machine Learning Research (2020)
5. Fast sampling with Gaussian scale-mixture priors in high-dimensional regression
A. Bhattacharya, A. Chakraborty, and B.K. Mallick. Biometrika (2016).
Statistical theory for variational inference
I. Ghosh, A. Bhattacharya, and D. Pati. Under revision.
Structured Variational Inference in Bayesian State-Space Models
H. Wang, Y. Yang, D. Pati, and A. Bhattacharya. AISTATS 2022 (forthcoming)
B.S. Guha, A. Bhattacharya, and D. Pati. ICTAI 2021
S. Plummer, S. Zhou, D. Pati, A. Bhattacharya, and D.B. Dunson. AISTATS 2021
Y, Yang, D. Pati, and A. Bhattacharya. The Annals of Statistics (2020)
A. Bhattacharya, D. Pati. and Y. Yang. The Annals of Statistics (2019)
D. Pati, A. Bhattacharya, and Y. Yang. AISTATS 2018
Bayesian model selection
D. Rossell, O. Abril, and A. Bhattacharya. The Journal of the Royal Statistical Society (Series B) (2021)
E. Chuu, D. Pati, and A. Bhattacharya. AISTATS 2021.
J. Geng, A. Bhattacharya, and D. Pati. Journal of the American Statistical Association (2019)
M. Shin, A. Bhattacharya, and V. E. Johnson. Statistica Sinica (2018)
Gaussian process priors
Y. Yang, A. Bhattacharya, and D. Pati. Under revision
D. Pati, A. Bhattacharya, and G. Cheng. JMLR (2015)
A. Bhattacharya, D. Pati, and D.B. Dunson. The Annals of Statistics (2014)