Research

Interests

My research program sits at the intersection of statistical methodology and applied public-health research. Three overlapping strands:

Cancer disparities & early-onset colorectal cancer. A line of work I’ve been building since my EVMS days, extending through several intramural and extramural grants. I look at how neighborhood characteristics, environmental exposures, and structural barriers shape stage-at-diagnosis and treatment patterns — with particular attention to young-onset cases in underserved populations.

Machine learning in health outcomes. Over the past several years I’ve been applying machine-learning approaches to problems where classical regression struggles: high-dimensional predictors, complex interactions, and data quality issues typical of registry-based and population-based studies. Recent work applies these methods to colorectal cancer outcomes, breast cancer recurrence, and cancer risk prediction across socioeconomic gradients.

Methods for confounding & propensity scores. My doctoral work on propensity-score methods when association is measured by AUC remains active — in subsequent publications I’ve shown that naive propensity-score adjustment can bias the conditional AUC, a subtle but consequential finding for diagnostic-accuracy studies in observational data.

By the numbers: 20 peer-reviewed journal articles, 19 published abstracts, 54 conference presentations, 8 invited talks. Principal Investigator on 9 awarded grants in cancer, machine-learning, and health-disparities research.

Selected Publications

Selected grants

Current projects

Dissertation committee chair

Chairing dissertation committees in the PhD in Health Services Research program at ODU:


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