# positions[].organizations[].parent[].label
Texas A&M Engineering Experiment Station (TEES)

# overview
Bayesian nonparametrics, contingency tables, covariance estimation, factor models, Gaussian process regression, high-dimensional data, network data, shrinkage priors, tensor decompositions, variable selection

# keywords[]
0801 Artificial Intelligence And Image Processing

# educationAndTraining[].organization.label
Indian Statistical Institute - (Kolkata, India)

# educationAndTraining[].organization.label
Indian Statistical Institute - (Kolkata, India)

# educationAndTraining[].organization.label
Duke University - (Durham, North Carolina, United States)

# awardsAndHonors[].label
Faculty Early Career Development (CAREER) Program

# publications[].label
Impaired limbic gamma oscillatory synchrony during anxiety-related behavior in a genetic mouse model of bipolar mania.

# publications[].label
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression.

# publications[].label
Bayesian Model Selection Based on Proper Scoring Rules - Comment

# publications[].label
Sub-optimality of some continuous shrinkage priors

# publications[].label
Hairline Fracture Detection using MRF and Gibbs Sampling

# publications[].publisher
Institute of Electrical and Electronics Engineers (IEEE)

# publications[].label
Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior

# publications[].publisher
Journal of Machine Learning Research - JMLR

# publications[].label
Simplex Factor Models for Multivariate Unordered Categorical Data.

# publications[].label
TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS.

# publications[].label
Adaptive Bayesian inference in the Gaussian sequence model using exponential-variance priors

# publications[].label
Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors

# publications[].label
Bayesian factorizations of big sparse tensors.

# publications[].label
Nonparametric Bayes modeling for case control studies with many predictors.

# publications[].label
Dirichlet-Laplace priors for optimal shrinkage.

# publications[].label
POSTERIOR CONTRACTION IN SPARSE BAYESIAN FACTOR MODELS FOR MASSIVE COVARIANCE MATRICES

# researcherOn[].label
Nutrition, Biostatistics and Bioinformatics Training Grant

# researcherOn[].awardedBy.label
National Institutes of Health - (Bethesda, Maryland, United States)

# researcherOn[].label
Consistent Variable Selection in p>>n Settings

# researcherOn[].awardedBy.label
National Institutes of Health - (Bethesda, Maryland, United States)

# researcherOn[].label
Enhanced Statistical Learning for Physical Systems Exploiting Non-Standard Constraints

# researcherOn[].awardedBy.label
National Science Foundation - (Arlington, Virginia, United States)

# researcherOn[].label
Prior Calibration and Algorithmic Guarantees Under Parameter Restrictions

# researcherOn[].awardedBy.label
National Science Foundation - (Arlington, Virginia, United States)

# researcherOn[].label
Bayesian inference for high-dimensional covariance matrices and tensors

# researcherOn[].awardedBy.label
United States Department of the Navy - (Arlington, Virginia, United States)

# researcherOn[].label
CAREER: Bayesian Generalized Shrinkage: An Encompassing Model Approach

# researcherOn[].awardedBy.label
National Science Foundation - (Arlington, Virginia, United States)

# researcherOn[].label
Collaborative Research: Scalable Bayesian Methods for Complex Data with Optimality Guarantees

# researcherOn[].awardedBy.label
National Science Foundation - (Arlington, Virginia, United States)

# streetAddress
Texas A&M University; Department Of Statistics; 3143 TAMU

# organizations[]
Texas A&M Engineering Experiment Station (TEES)

# schools[]
Texas A&M Engineering Experiment Station (TEES)

# etdChairOf[].label
Chakraborty, Antik (2018-08). Bayesian Shrinkage: Computation, Methods and Theory. Doctoral Dissertation.

# etdChairOf[].label
Zhou, Shuang (2020-07). Semi-Parametric Bayesian Function Estimation Using Gaussian Process Transformations. Doctoral Dissertation.

# etdChairOf[].label
Acharyya, Satwik (2020-07). Case Studies in Hierarchical Bayesian Modeling. Doctoral Dissertation.

# etdChairOf[].label
Shin, Minsuk (2017-07). Priors for Bayesian Shrinkage and High-Dimensional Model Selection. Doctoral Dissertation.

# etdChairOf[].label
Larsen, Allyson Elaine (2020-05). Approximation Schemes to Simplify Posterior Computation. Doctoral Dissertation.