My methodological work centers on marginal modeling approaches — particularly penalized GEE and QIF — for spatially correlated repeated-measures data with excess zeros. Substantively, I apply these methods to characterize the built food environment near schools and examine socioeconomic patterning of health-relevant exposures across geographic scales.

I have served as lead biostatistician on 5+ NIH-funded longitudinal health and RWE studies, managing large-scale relational datasets integrating EHR, geospatial, retail environment, and longitudinal cohort data. I apply causal inference methods (PSM, IPW, RDD, IV), latent class and transition models, Bayesian hierarchical models, and survival analysis — and develop R packages and R Shiny dashboards for non-technical stakeholders.

Current Position

Doctoral Research Fellow & Lead Biostatistician
Biostatistics for Social Impact Lab, Drexel University  ·  2022–Present

• Lead biostatistician on 5 NIH-funded RWE and longitudinal health studies; design SAPs, manage 3M+ record datasets, and deliver analytical insights to multidisciplinary teams
• Developing fusion learning–based penalized GEE framework for spatially correlated, zero-inflated count outcomes; lead simulation studies evaluating bias under complex data scenarios
• Apply causal inference (PSM, IPW, IV, RDD), latent class/transition models, Bayesian hierarchical models, and survival analysis; develop R packages and R Shiny dashboards

Research Interests

Technical Skills

NIH-Funded Projects

Additional collaborations: MESA Study (latent transition analysis, retail environment & HbA1c)  ·  All of Us Research Program (school racial diversity; Hispanic adult obesity)