UIUC’s Statistics PhD blends rigorous probability foundations with high-performance algorithms and responsible AI. Doctoral candidates derive non-parametric inference for graph data, accelerate Bayesian sampling on GPUs, and quantify fairness trade-offs in healthcare prediction. Collaborations with NCSA and industry labs open vast, messy datasets for discovery.
Graph neural-network uncertainty quantification via Bayesian GNNs
Causal inference with time-series cross-validation for policy shocks
Differentially private probabilistic programming for sensitive data
Optimal transport-based two-sample tests in genomics
Scalable Hamiltonian Monte Carlo on GPU tensor cores
Fairness constraints in survival models for transplant allocation
Topological data-analysis confidence intervals for climate extremes
Meta-learning priors for small-area disease prevalence estimation
Grant proposal for open-source library on Bayesian deep ensembles
White paper on ethical auditing of predictive policing models
Interactive Shiny app teaching simulation-based inference
Tensor decomposition of multi-modal neuroimaging data
Robust regression under adversarial data poisoning
Policy memo on statistical literacy for lawmakers
Long-memory time-series modeling of cryptocurrency volatility
Citizen-science workshop on data visualization for community activists
Invent tomorrow’s statistical tools with UIUC Statistics.
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