# 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.

# keywords[]
Symmetrized Probit Stick Breaking Process

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

# publications[].label
Algorithm xxx: A Covariate-Dependent Approach to Gaussian Graphical Modeling in R

# publications[].publisher
Association for Computing Machinery (ACM)

# publications[].label
Variable selection using shrinkage priors

# publications[].label
Shape-Constrained and Unconstrained Density Estimation Using Geometric Exploration

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

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

# 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
Bayesian nonparametric regression with varying residual density.

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

# publications[].label
Bayesian Multiscale Modeling of Closed Curves in Point Clouds.

# publications[].label
A location-mixture autoregressive model for online forecasting of lung tumor motion

# publications[].label
ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES

# publications[].label
Bayesian partial linear model for skewed longitudinal data.

# publications[].label
Compressed Covariance Estimation with Automated Dimension Learning

# publications[].label
Probabilistic Community Detection With Unknown Number of Communities

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

# publications[].label
Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors.

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

# publications[].label
Bayesian geostatistical modelling with informative sampling locations.

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

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

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