The Biostatistics PhD at Harvard Chan trains methodologists to craft scalable, robust inference for genomics, environmental exposures, and health-policy evaluation. Students develop novel Bayesian non-parametrics, causal-ML estimators, and privacy-preserving algorithms while collaborating with Dana-Farber trials and national cohort studies. Graduates drive analytics in academia, FDA, and tech-health giants.
Non-parametric Bayesian model for single-cell RNA-seq trajectories
Differential privacy in federated multi-site clinical trials
Targeted-ML estimator for policy interventions on air pollution
Sparse additive hazard model for high-frequency ICU data
Surrogate-endpoint validation using hierarchical meta-analysis
Deep generative models synthesizing rare-disease cohorts
Graphical models capturing gene–environment interactions
Adaptive-sequential design for platform oncology trials
Open-source R package for doubly robust survival estimators
Ethics memo on fairness in predictive-risk scores for sepsis
Visualization dashboard of uncertainty in climate-health projections
Workshop on reproducible research workflows with Quarto and GitHub
Build the statistical engines powering precision health.
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