The Harvard Chan MS in Biostatistics fuses probability theory, high-dimensional modeling, and computing in R and Python to power genomic, clinical-trial, and health-policy breakthroughs. Students decode CRISPR screens, build Bayesian adaptive trial designs, and optimize causal-machine-learning pipelines on cloud clusters. Close mentorship alongside the Dana-Farber Cancer Institute positions graduates for pharma analytics, FDA review science, and doctoral study pushing the frontier of precision health.
Hierarchical model estimating treatment heterogeneity in oncology trials
Deep-learning imputation of missing EHR lab values
GWAS meta-analysis pipeline for multi-ethnic cohorts
Adaptive randomization algorithm for rare-disease studies
Simulation study comparing causal-forest vs. TMLE estimators
Shiny dashboard visualizing longitudinal patient-reported outcomes
Design of privacy-preserving federated analyses across hospitals
Spline-based survival model for transplant graft longevity
Open-source R package for robust sandwich variance estimators
Cluster-randomized-trial power calculator web tool
Network meta-analysis of COVID antivirals
Capstone on algorithmic fairness in risk-prediction tools
Interactive workshop on reproducible pipelines with Quarto
Ethics memo on synthetic-control arms in oncology
Data-visualization atlas of gene–environment interactions
Podcast interviewing statisticians on vaccine-safety monitoring
Workshop teaching clinicians basics of mixed-models
Cloud deployment template for scalable genomics workflows
Turn complex biomedical data into life-saving insight.
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