I provide advanced epidemiology, biostatistics, and applied data science services for organizations that need rigorous analysis of health and population-level data. My work focuses on statistical modeling, surveillance analytics, and reproducible coding workflows to support research, evaluation, and decision-making.
I hold a PhD in Health Services Research with specialization in public health policy, an MS in Health Care Administration, and formal training in epidemiology and healthcare analytics. My academic background emphasizes applied quantitative methods, health economics, and policy-relevant analysis rather than purely theoretical work.
My services include epidemiologic study design, disease surveillance analysis, statistical modeling, trend and disparity analysis, forecasting and projection, economic burden modeling, and program evaluation. I work extensively with large administrative datasets, registries, survey data, and linked data sources. I routinely use SAS, R, Python, SQL, and Excel VBA to develop reproducible pipelines, analytic code, and technical documentation. Methods commonly include regression modeling, survival analysis, hierarchical and Bayesian models, Monte Carlo simulation, decision analytic frameworks, and probabilistic sensitivity analysis.
I bring more than 20 years of experience in applied epidemiology and population health analytics across cancer surveillance, chronic disease, tobacco control, stroke, and health services research. I do not provide medical or clinical advice. My role is technical analysis, modeling, and interpretation at the population, program, or policy level.