UC’s PhD in Statistics offers rigorous training in probability, inference, and computational methods, preparing scholars for data-science leadership and academic research.
Hierarchical Bayesian model estimating COVID-19 excess mortality by county
Sparse Gaussian-process emulator for climate-model downscaling
Causal forest analysis of education-policy impacts on earnings
Tensor decomposition of single-cell RNA-seq count matrices
Deep generative model producing privacy-preserving synthetic health records
Sequential Monte Carlo algorithm accelerating portfolio risk estimation
Functional data analysis of wearable-sensor gait waveforms
Change-point detection in satellite fire-radiative-power series
Approximate Bayesian computation for ancient-DNA demographic models
Adaptive design simulation for platform clinical trials
Network meta-analysis of dietary interventions on lipid outcomes
Empirical likelihood methods for survey non-response bias
Markov-chain mixing diagnostics for high-dimensional HMC
Interactive Shiny app visualizing bootstrap distributions
White paper on statistical standards for AI fairness audits
VR tutorial demonstrating the central-limit theorem via particles
Workshop teaching tidyverse and ggplot through climate datasets
Student-run consulting clinic for nonprofits lacking data capacity
Graphical model of wildfire spread incorporating spatial autocorrelation
Conference on reproducible research and open-source software
Model uncertainty and extract insight with UC’s computation-rich PhD.
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