Georgia Tech’s Statistics PhD fuses rigorous probability with scalable computation. Scholars derive Bernstein-von Mises limits for deep nets, craft distributed M-estimators for terabyte sensor feeds, and deploy causal forests in CDC pandemic dashboards. Dual appointments in Math, ISyE, and CS foster cross-pollination that lands alumni at CDC leadership, hedge-fund quants, and top-tier faculty seats.
Scalable Hamiltonian Monte-Carlo on GPUs for climate models
Differential-privacy guarantees for federated medical data analysis
Graphical causal discovery in high-frequency financial markets
Post-selection inference for adaptive Lasso in genomics
Optimal stopping theory for real-time A/B tests with streaming data
Bayesian nonparametric clustering of single-cell transcriptomes
Sparse covariance estimation for neuro-imaging connectomes
Knockoff filter construction for deep feature selection
Bootstrap uncertainty quantification in reinforcement-learning policies
Change-point detection in multivariate anomaly streams
Likelihood-free inference using neural density estimators
Robust M-estimation under adversarial data poisoning
Conformal prediction intervals for autonomous-vehicle perception
Empirical Bayes shrinkage for small-area COVID prevalence
Extreme-value theory of compound climate catastrophes
Advance theory and computation to unlock trustworthy insight from massive data.
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