Career Profile
I am a PhD student in Statistics at Texas A&M with hands-on experience in predictive modeling, uncertainty quantification, and Bayesian methods. My research focuses on developing and applying Bayesian approaches for prevalence estimation, diagnostic accuracy evaluation, and latent class modeling, with growing interest in applications to clinical trial design and simulation-based inference.
Experiences
- Developed and deployed credible intervals for predictive models, improving reliability of forecast.
- Designed repeatability and reproducibility experiments, strengthening product validation.
- Built predictive models linking chemical composition to transmission fluid performance to guide the data-driven product development decisions.
- Applied high-dimensional regression and clustering methods to DNA methylation data, identifying biomarkers linked to disease pathways.
- Analyzed NHANES data to evaluate dietary risk factors for hypertension in pregnancy, contributing to peer-reviewed publications.
Projects
Selected projects demonstrating applications of Bayesian modeling, causal inference, and reproducible methods in health and clinical trial contexts.
Applied Statistical Modeling
Selected projects highlighting advanced modeling frameworks for complex data, including zero-inflated count regression and spatio-temporal clustering analyses.
Publications
Below is a selection of my published work in biological and statistical research. My contributions span areas such as epigenomics, metabolic disease, and quantitative methods for diagnostic accuracy and prevalence estimation. These publications reflect my interdisciplinary approach at the intersection of data science, biostatistics, and biomedical research.