# overview
My research broadly focuses on robust and efficient statistical inference with high dimensional and/or incomplete observational data that arise frequently in the modern "big data" era in a variety of scientific disciplines. Most of my work is inspired by real world scientific questions and statistical challenges arising from the analysis of such complex data. Through my work, I try to answer such questions through principled formulation of the underlying statistical problem, and developing methods with provable guarantees and scalable implementations, so that they actually serve a meaningful purpose for practitioners faced with these questions.

Some of my main research interests include: Semi-supervised learning; Causal inference and missing data; Transfer learning; High dimensional semi-parametric inference; Bayesian semi-parametric inference; Applications in large-scale biomedical studies.

# keywords[]
High-dimensional Linear Regression And Lasso

# keywords[]
Hypoxia-Inducible Factor 1, Alpha Subunit

# educationAndTraining[].organization.label
Harvard University - (Cambridge, Massachusetts, United States)

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

# educationAndTraining[].organization.label
Harvard University - (Cambridge, Massachusetts, United States)

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

# awardsAndHonors[].label
Texas A&M Institute of Data Science Career Initiation Fellow

# awardsAndHonors[].label
Joshua E. Neimark Memorial Travel Assistance Award

# awardsAndHonors[].label
National Board of Higher Mathematics (NBHM) Postgraduate Fellowship

# publications[].label
Bayesian Semi-supervised Inference via a Debiased Modeling Approach

# publications[].label
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric   Framework

# publications[].label
Tail Bounds for Canonical $U$-Statistics and $U$-Processes with Unbounded Kernels

# publications[].label
Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects

# publications[].label
A General Framework for Treatment Effect Estimation in Semi-Supervised   and High Dimensional Settings

# publications[].label
Inference for Individual Mediation Effects and Interventional Effects in   Sparse High-Dimensional Causal Graphical Models

# publications[].label
Moving beyond sub-Gaussianity in high-dimensional statistics: applications in covariance estimation and linear regression

# publications[].label
Hypoxic tumor microenvironment: Implications for cancer therapy.

# publications[].label
Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index   Models for Binary Outcomes

# publications[].label
Estimating Average Treatment Effects with a Double-Index Propensity Score

# publications[].label
Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects

# publications[].label
EFFICIENT AND ADAPTIVE LINEAR REGRESSION IN SEMI-SUPERVISED SETTINGS

# publications[].label
The Decaying Missing-at-Random Framework: Doubly Robust Causal Inference with Partially Labeled Data

# publications[].label
Semi-Supervised Quantile Estimation: Robust and Efficient Inference in   High Dimensional Settings

# publications[].label
Double Robust Semi-Supervised Inference for the Mean: Selection Bias   under MAR Labeling with Decaying Overlap

# publications[].label
Bayesian semi-supervised Inference via a debiased modeling approach

# publications[].label
A Poisson regression model for association mapping of count phenotypes

# publications[].label
Semi-supervised estimation of covariance with application to phenome-wide association studies with electronic medical records data.

# researcherOn[].label
Modern Problems with Big Data: Efficient Statistical Inference in Semi-supervised Settings

# researcherOn[].awardedBy.label
Texas A&M University - (College Station, Texas, United States)

# researcherOn[].awardedBy.preferredLabel
Texas A&M University - College of Arts & Sciences (STRP Grant)

# researcherOn[].label
NSF DMS-2113768 - Robust Statistical Inference in Large Scale Semi-Supervised Settings

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

# researcherOn[].label
College of Arts & Sciences Internal Conference Travel Grant

# researcherOn[].awardedBy.label
Texas A&M University - (College Station, Texas, United States)

# researcherOn[].label
Texas A&M Institute of Data Science (TAMIDS) Career Initiation Fellowship

# researcherOn[].awardedBy.label
Texas A&M University - (College Station, Texas, United States)

# researcherOn[].label
Texas A&M Triads for Transformation (T3) - "Large Scale Statistical Inference For Big Financial Data"

# researcherOn[].awardedBy.label
Texas A&M University - (College Station, Texas, United States)

# streetAddress
Dept. of Statistics, Texas A&M University; 155 Ireland Street; Blocker Building, Room 401D

# inTheNews[].label
2022 TAMIDS Career Initiation Fellow Awards

# inTheNews[].label
Workflow Workshop: Tutorial on Causal Inference

# inTheNews[].label
TAMIDS Seminar Series: Causal Inference from Observational Data