Research
My research focuses on bias-robust Bayesian evidence synthesis and reproducible statistical workflows for applied literatures where publication bias, small-study effects, and selective reporting make conventional summaries difficult to interpret.
Current work
Quantifying Evidential Rigor in Meta-Analytic Corpora
A Simulation-Characterized, Bias-Robust Bayesian Workflow with a Nutrition Case Study.
This preprint introduces a corpus-scale Bayesian evidential-audit workflow for meta-analytic evidence. It fits a matched Bayesian random-effects baseline alongside a bias-aware, model-averaged ensemble, then summarizes evidential yield with a rigor estimand: resolved evidence for an effect or for no effect, together with the absence of an explicit modeled bias component. The workflow is characterized with simulation and resampling designs and demonstrated on a nutrition intervention meta-analysis corpus. The companion repository supports reproducibility and adaptation of the workflow to other literatures.
Research interests
Bias-robust Bayesian meta-analysis. Model-averaged Bayes factors, selection models, heterogeneity, and evidence for or against effects after accounting for bias.
Publication bias and evidential fragility. Selective reporting, small-study effects, threshold behavior, and how these distort applied literatures.
Corpus-scale evidential auditing. Summarizing evidential yield across many meta-analyses, rather than one effect at a time.
Nutrition and health evidence synthesis. Applying bias-aware Bayesian methods to intervention meta-analyses in nutrition, fitness, and health.
Reproducible statistical workflows. R, Quarto, Git, simulation studies, public repositories, and transparent research pipelines.
Statistics education and AI-supported learning. Introductory statistics, Bayesian intuition, reproducible work, and the responsible use of AI assistants in mathematical and statistical learning.