Dissertation Ideas for Ph.D. Statistics Students

Bayesian Nonparametric Models for Complex Time Series Forecasting

Causal Inference in High-Dimensional Settings Using Doubly Robust Estimation

Graphical Models and Structure Learning for Genomic Networks

Sequential Decision Making with Contextual Bandits in Healthcare

Functional Data Analysis for Longitudinal Biomarker Trajectories

Robust Estimation Techniques for Outlier-Prone Financial Datasets

Semi-Supervised Learning for Rare Event Detection in Manufacturing

Survival Analysis with Time-Varying Covariates in Cancer Prognostics

Adaptive Experimental Design Using Information-Theoretic Criteria

Empirical Likelihood Methods for Survey Data Inference

Change Point Detection in Multivariate Time Series Data

High-Dimensional Covariance Estimation and Sparse PCA

Bayesian Hierarchical Modeling for Ecological Count Data

Bootstrap Techniques for Uncertainty Quantification in Deep Learning

Network Meta-Analysis Using Bayesian Graphical Models

Multiscale Modeling of Spatially Correlated Environmental Data

Latent Variable Models for Psychological and Social Survey Analysis

Sparse Additive Models for Functional Genomics

Tensor Decomposition Methods in Spatiotemporal Data Analysis

Nonparametric Bayesian Models for Topic Modeling in Text Data

Federated Learning Algorithms for Distributed Statistical Inference

Optimal Stopping Rules in Online Experimentation Frameworks

Empirical Bayes Shrinkage Estimators in Small Area Statistics

Explainable AI with Probabilistic Graphical Interpretations

Time-Varying Coefficient Models for Streaming Data Analytics