I am a biostatistician and data scientist specializing in Bayesian modeling, machine learning, and large-scale biomedical data analysis, with a focus on translating complex data into robust, decision-ready insights.
I currently work as a Postdoctoral Associate in Psychiatry at Yale School of Medicine, where I lead analyses of biobank-scale genomic and clinical datasets—including the Million Veteran Program (MVP) and UK Biobank—to study psychiatric and substance use disorders. My work integrates statistically rigorous methodology with applied modeling to characterize genetic risk architecture, longitudinal trajectories, and clinically relevant patterns in real-world data.
My experience includes: - Bayesian and frequentist methods for high-dimensional data
- Machine learning and predictive modeling
- Statistical genetics and environmental mixture analysis
- Scalable, secure, and reproducible data pipelines
- Open-source software development (R packages and analytical tools)
Across projects, I collaborate closely with clinicians, geneticists, and data scientists to ensure results are interpretable, reproducible, and actionable. I’m motivated by work that bridges methodological rigor and real-world impact, especially in precision medicine, clinical research, and data-driven decision-making.