UIUC’s Statistics MS blends rigorous probability, high-performance computing, and interdisciplinary collaboration. Students derive asymptotics by day and deploy Bayesian models on GPUs by night, partnering with climate scientists, sports analysts, and fintech quants. Electives in causal inference, deep generative models, and responsible AI ensure graduates wield statistics wisely and impactfully.
Causal forest estimating policy impact on renewable-energy adoption
Bayesian hierarchical model of athlete injury risk over seasons
Variational inference for large-scale topic modeling of legal documents
Spatial point-process analysis of wildfire ignition patterns
Deep probabilistic forecasting of cryptocurrency volatility
Differential-privacy mechanism for census microdata release
Adaptive experiment design in A/B/n web trials
Tensor decomposition of brain-connectome networks
Meta-analysis automation with NLP extraction of effect sizes
Copula-based stress tests for climate-related financial risk
Grant proposal for open-source MCMC on heterogeneous clusters
Ethics brief on algorithmic fairness metrics in policing data
Interactive Shiny dashboard teaching bootstrapping concepts
Zero-inflated mixed model for microbiome count data
Stochastic gradient Langevin dynamics sampler benchmarking
Podcast demystifying p-values for policy makers
Survival analysis of e-bike battery failure times
Graphical LASSO study of gene regulatory networks
Advance from data to defensible insight with Statistics at UIUC.
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