Yale’s Ph.D. in Statistics and Data Science equips scholars with the analytical and computational tools to solve real-world problems. Below are impactful thesis topics aligned with current industry and research needs.
Bayesian Hierarchical Models in Public Health Forecasting
Causal Inference Methods for Observational Big Data
Privacy-Preserving Algorithms in Federated Data Systems
Sparse Modeling Techniques in High-Dimensional Genomics
Fairness Metrics in Machine Learning Classifiers
Graph-Based Semi-Supervised Learning Techniques
Robust Statistical Techniques for Outlier Detection
Ethical Frameworks for Algorithmic Bias Mitigation
Time Series Modeling for Climate Change Indicators
Reinforcement Learning for Dynamic Treatment Regimes
Scalable Monte Carlo Methods for Large-Scale Data
Survival Analysis with Competing Risks in Medical Studies
Deep Learning Interpretability via Shapley Decomposition
Adaptive Sampling Strategies in Active Learning
Missing Data Imputation using Generative Models
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