Dissertation Ideas for Statistics Doctoral Students

Bayesian Nonparametric Models for Dynamic Risk Forecasting

High-Dimensional Variable Selection in Genomic Data

Statistical Inference for Reinforcement Learning Algorithms

Multilevel Causal Inference with Unmeasured Confounding

Sparse Estimation Techniques in Functional Data Analysis

Robust Estimation in the Presence of Heavy-Tailed Distributions

Statistical Frameworks for Federated Biomedical Learning

Inference in Network Models with Missing Edges

Statistical Foundations of Deep Generative Models

Change-Point Detection in Streaming Time Series Data

Density Ratio Estimation for Distribution Shift Scenarios

Uncertainty Quantification in Ensemble Learning Models

Semiparametric Approaches for Instrumental Variable Analysis

Bootstrap Methods for Spatially Correlated Observations

Optimal Experimental Design in Adaptive Clinical Trials

Graph-Based Testing Procedures for Large-Scale Social Networks

Fairness-Aware Statistical Modeling for Policy Evaluation

Calibration and Coverage of Conformal Prediction Regions

Hierarchical Bayesian Models for Small Area Estimation

Double Robust Estimators in Causal Mediation Analysis

Covariate Balancing Techniques for Observational Studies

Scalable Variational Inference for Large-Scale Gaussian Processes

Nonparametric Hypothesis Testing in Manifold Spaces

Bias-Variance Tradeoff in Modern Ensemble Forecasting

Statistical Methodologies for Multi-Omics Data Integration

Tensor Regression Models in Spatiotemporal Epidemiology

Selective Inference After Model Selection Procedures

Randomization-Based Inference in Clustered Designs

Adaptive Shrinkage Priors for Hierarchical Modeling

Time-Varying Coefficient Models in Financial Forecasting