Pati, Debdeep individual record
Associate Professor
Positions:
overview

My research centers around methodological aspects of Bayesian statistics and its application to large scale complex data. I am particularly focused on developing methodology in a broad range of areas including semi-parametric density regression, shrinkage priors for anisotropic function estimation, variable selection with non-Gaussian errors, massive covariance matrix estimation, surface reconstruction and imaging and modeling shapes of non-Euclidean objects. I enjoy developing methodology that has an immediate motivation and impact to a particular application area, while being broadly applicable and leading to foundational questions. In the Bayes paradigm this often involves developing new classes of flexible prior distributions for densities, conditional densities, functions, sparse vectors, matrices or tensors. It is fascinating to explore the structure of the spaces on which the priors are supported while studying how the posterior concentrates as increasing amounts of data are collected. Studying these spaces becomes more challenging outside of unconstrained Euclidean spaces, such as in studying closed surfaces and other shapes, and when the dimension explodes. While Bayesian hierarchical models offer an unified and coherent framework for structured modeling and inference, two key challenges persist. First, as one moves away from simple parametric models, understanding properties of a posterior distribution poses a stiff challenge. Second, even if the true posterior has desirable properties, sampling from the posterior distribution in large scale problems commonly face scalability issues. This is relevant both for high-dimensional and big data problems. My research aims at addressing these challenges simultaneously, developing new theory to evaluate the associated procedures and developing scalable and highly efficient algorithms for Bayesian computation.

education and training
selected publications
Academic Articles20
  • Bhattacharya, A., Pati, D., & Yang, Y. (2019). BAYESIAN FRACTIONAL POSTERIORS. Annals of Statistics. 47(1), 39-66.
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  • Sarkar, A., Pati, D., Chakraborty, A., Mallick, B. K., & Carroll, R. J. (2018). Bayesian Semiparametric Multivariate Density Deconvolution. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. 113(521), 401-416.
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  • Sabnis, G., Pati, D., & Bhattacharya, A. (2018). Compressed Covariance Estimation with Automated Dimension Learning. Sankhya A - Mathematical Statistics and Probability. 1-16.
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  • Geng, J., Bhattacharya, A., & Pati, D. (2018). Probabilistic Community Detection With Unknown Number of Communities. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. 1-13.
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  • Norets, A., & Pati, D. (2017). ADAPTIVE Bayesian ESTIMATION of CONDITIONAL DENSITIES. ECONOMETRIC THEORY. 33(4), 980-1012.
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  • Li, H., & Pati, D. (2017). Variable selection using shrinkage priors. COMPUTATIONAL STATISTICS & DATA ANALYSIS. 107, 107-119.
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  • Bhattacharya, A., Dunson, D. B., Pati, D., & Pillai, N. S. (2016). Sub-optimality of some continuous shrinkage priors. STOCHASTIC PROCESSES AND THEIR APPLICATIONS. 126(12), 3828-3842.
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  • Pati, D., & Bhattacharya, A. (2015). Adaptive Bayesian inference in the Gaussian sequence model using exponential-variance priors. Statistics & Probability Letters. 103, 100-104.
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  • Zhang, Z., Pati, D., & Srivastava, A. (2015). Bayesian clustering of shapes of curves. JOURNAL OF STATISTICAL PLANNING AND INFERENCE. 166, 171-186.
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  • Tang, Y., Sinha, D., Pati, D., Lipsitz, S., & Lipshultz, S. (2015). Bayesian partial linear model for skewed longitudinal data.. BIOSTATISTICS. 16(3), 441-453.
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  • Bhattacharya, A., Pati, D., Pillai, N. S., & Dunson, D. B. (2015). Dirichlet-Laplace Priors for Optimal Shrinkage. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. 110(512), 1479-1490.
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  • Pati, D., Bhattacharya, A., & Cheng, G. (2015). Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior. Journal of Machine Learning Research. 16, 2837-2851.
  • Cervone, D., Pillai, N. S., Pati, D., Berbeco, R., & Lewis, J. H. (2014). A location-mixture autoregressive model for online forecasting of lung tumor motion. Annals of Applied Statistics. 8(3), 1341-1371.
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  • Bhattacharya, A., Pati, D., & Dunson, D. (2014). ANISOTROPIC FUNCTION ESTIMATION USING MULTI-BANDWIDTH GAUSSIAN PROCESSES. Annals of Statistics. 42(1), 352-381.
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  • Gu, K., Pati, D., & Dunson, D. B. (2014). Bayesian Multiscale Modeling of Closed Curves in Point Clouds. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. 109(508), 1481-1494.
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  • Sarkar, A., Mallick, B. K., Staudenmayer, J., Pati, D., & Carroll, R. J. (2014). Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS. 23(4), 1101-1125.
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  • Pati, D., & Dunson, D. B. (2014). Bayesian nonparametric regression with varying residual density. Annals of the Institute of Statistical Mathematics. 66(1), 1-31.
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  • Pati, D., Bhattacharya, A., Pillai, N. S., & Dunson, D. (2014). POSTERIOR CONTRACTION IN SPARSE BAYESIAN FACTOR MODELS FOR MASSIVE COVARIANCE MATRICES. Annals of Statistics. 42(3), 1102-1130.
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  • Pati, D., Dunson, D. B., & Tokdar, S. T. (2013). Posterior consistency in conditional distribution estimation. Journal of Multivariate Analysis. 116, 456-472.
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  • Pati, D., Reich, B. J., & Dunson, D. B. (2011). Bayesian geostatistical modelling with informative sampling locations. BIOMETRIKA. 98(1), 35-48.
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Chapters1
  • Zhang, Z., Pati, D., & Srivastava, A. (2015). Bayesian shape clustering. Nonparametric Bayesian Inference in Biostatistics. (pp. 57-76). Springer International Publishing.
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Conference Papers1
Email
debdeep@tamu.edu
First Name
Debdeep
Last Name
Pati
mailing address
Texas A&M University; Department Of Statistics; 3143 TAMU
College Station, TX 77843-3143
USA