Stanford’s Ph.D. in Statistics prepares researchers to build new methodologies and apply statistical frameworks to solve real-world problems. These dissertation topics span modern statistical theory, computation, machine learning, and applications in science and industry.
Bayesian Inference with Nonparametric Priors in Complex Systems
Causal Effect Estimation in the Presence of Hidden Confounders
Robust High-Dimensional Regression under Adversarial Perturbations
Privacy-Preserving Statistical Analysis Using Differential Privacy
Sparse Estimation Techniques for Gene Expression Data
Monte Carlo Tree Search for Adaptive Clinical Trials
Semi-Supervised Learning with Statistical Guarantees
Optimal Stopping Theory in Sequential Data Settings
Survival Analysis with Dynamic Time-Varying Covariates
Graphical Models for Analyzing Social Network Structures
Multi-Armed Bandits for Online Decision Making in E-Commerce
Bayesian Hierarchical Models for Healthcare Outcome Prediction
Covariate Balancing in Observational Studies with Propensity Scores
Uncertainty Quantification in Neural Networks via Bayesian Deep Learning
Resampling Methods for Small-Sample Inference
Statistical Calibration of Climate Simulation Models
Functional Data Analysis for Wearable Health Sensor Streams
Markov Chain Mixing in High-Dimensional Latent Variable Models
Nonparametric Density Estimation Using Spline-Based Methods
Empirical Likelihood for Time Series with Structural Breaks
From Bayesian frameworks to Monte Carlo simulations, Collexa helps Stanford Statistics Ph.D. candidates refine models, implement reproducible code, and publish impactful research.
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