Duke’s Statistical Science Ph.D. trains probabilists who push theory and practice—developing Bayesian nonparametrics, causal graphs, and federated learning engines that power health, climate, and social-justice analytics. Annual Data4Good hackathons and policy residencies ensure methods land where they matter.
Hierarchical model forecasting dengue outbreaks from climate data
Causal-impact analysis of universal basic-income pilots
Tensor decomposition of fMRI time-series for depression biomarkers
Deep-ensemble uncertainty quantification in autonomous-vehicle vision
Differential-privacy library for municipal data portals
Spatial point-process study of urban gun-violence hotspots
AutoML pipeline selecting green-chemistry reaction routes
Meta-analysis app summarizing vaccine-trial efficacy
Reinforcement-learning algorithm balancing grid-battery dispatch
Graph neural network identifying poaching hotspots
Open-source GitHub action auto-checking project reproducibility
Policy brief on statistical standards for AI auditing
Citizen-science drone image classifier improving flood response
Explainable boosting machine detecting credit-card fraud
Turn uncertainty into actionable insight with Duke Stats.
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