CMU’s Stats & DS PhD combines rigorous probability theory, cutting-edge ML, and real-world collaboration. Students derive causal-inference guarantees, design privacy-preserving estimators, and partner with sports analytics, public-health, or fintech groups. A unique Teaching Practicum emphasizes communicating uncertainty to diverse audiences—from high-school classrooms to congressional hearings.
Bayesian non-parametric model for dynamic network community detection
Differential-privacy mechanism preserving rank statistics in census data
Counterfactual fairness estimator for algorithmic-lending decisions
Spectral regularization for low-rank matrix completion with heteroskedastic noise
Causal forest estimating treatment heterogeneity in vaccine-uptake trials
Online bootstrap confidence bands for streaming time-series forecasts
Hierarchical topic model tracking misinformation narratives
Multi-armed bandit allocating COVID testing resources across counties
Graphical lasso variants for precision-medicine biomarker selection
Zero-inflated Poisson mixed-model for micro-mobility trip counts
Bayesian model averaging for ensemble weather prediction systems
Shiny dashboard automating interpretability reports for regulators
Optimal experimental-design algorithm maximizing information under cost constraints
Green computation metric estimating energy cost of large-scale inference
Policy memo on statistical standards for AI risk-assessment disclosures
Shape reliable inference and impact policy with CMU Stats & DS.
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