Northwestern’s Statistics PhD trains theorists and data scientists to push boundaries in Bayesian computation, high-dimensional inference, and causal discovery. Students prove risk bounds for federated learning, develop MCMC on GPU clusters, and consult for biomedical researchers, hedge funds, and civic-tech groups.
Derive minimax rates for graphon estimation under node sampling
Implement Rao-Blackwellized particle filters for stochastic volatility models
Construct doubly robust estimators for heterogeneous treatment effects
Develop scalable Hamiltonian Monte Carlo for large neural-network posteriors
Analyze fairness constraints in uplift modeling for public-health interventions
Create a nonparametric test for change-points in streaming data
Model epidemic tipping points with branching-process approximations
Design adaptive experimental designs minimizing regret in A/B testing
Build a probabilistic programming framework integrating symbolic algebra
Estimate extreme quantiles of climate-risk losses via peaks-over-threshold
Prove oracle inequalities for sparse additive models with group lasso
Quantify uncertainty in network community detection using Bayesian SBM
Develop causal discovery algorithms leveraging invariant risk minimization
Simulate adaptive survey designs reducing bias in hidden populations
Write an R package implementing PAC-Bayesian bounds visualization
Advance statistical theory and impactful data science with Northwestern.
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