Stanford’s Ph.D. in Statistics trains future researchers in both rigorous statistical theory and cutting-edge applied analysis. These dissertation ideas explore advances in high-dimensional modeling, Bayesian methods, fairness in AI, and causal inference for real-world applications.
Causal Inference in Observational Healthcare Data Using Targeted Learning
Bayesian Nonparametric Models for Spatial Epidemiology
High-Dimensional Regression Techniques for Genomics Applications
Explainable Statistical Learning Models in Algorithmic Decision-Making
Robust Estimators Under Model Misspecification in Real-World Data
Graphical Models for Social Network Analysis with Missing Data
Bayesian Hierarchical Models for Longitudinal Clinical Trials
Post-Selection Inference in Regularized Linear Models
Differential Privacy in Statistical Data Publishing
Empirical Process Theory for Deep Neural Networks
Causal Discovery Algorithms for Time Series Data
Bayesian Optimization in High-Throughput Experimental Design
Ensemble Methods for Survival Data with Censoring
Statistical Foundations of Fairness in Machine Learning
Asymptotic Theory for Bootstrap Confidence Sets in Complex Models
Functional Data Analysis in Wearable Health Device Studies
Sparse Graph Estimation in Financial Time Series Modeling
Semi-Supervised Learning with Statistical Guarantees
Latent Variable Modeling for Behavioral Survey Data
Statistical Theory Behind Federated Learning Algorithms
Collexa supports Stanford Statistics Ph.D. candidates with theoretical development, simulation studies, R/Python-based pipelines, and academic publication preparation.
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