Bayesian analysis of COVID-19 Vaccine Efficacy
Bayesian StatisticsBayesian modeling and analysis to evaluate COVID-19 vaccine effectiveness using statistical inference methods.
Statistics · Public Health · Data Science
I'm currently studying Statistics and Biochemistry at the University of Washington, tackling problems in healthcare with data science and machine learning. My work spans clinical data analysis, AI-driven methods, and research-based methods, with a focus on building scalable, impactful tools.
Bayesian modeling and analysis to evaluate COVID-19 vaccine effectiveness using statistical inference methods.
Longitudinal analysis of coherence across M1U, S1U, and PFU during an orofacial texture task in rhesus macaques, comparing control, scopolamine, and nerve block conditions.
Shiny dashboard exploring vaccine coverage (DTP3, MCV1, BCG) against health expenditure trends, with clustering to surface regional and income-based patterns over time.
Fred Hutchinson Cancer Research Center
University of Washington (Arce-McShane Group)
University of Washington
Washington National Primate Center
I'm currently analyzing neural phase coherence in nonhuman primates to understand how different brain regions synchronize during texture-guided sensorimotor behavior. This work extends my earlier statistical analysis of coherence magnitudes by shifting into angular data and true time-series territory. In addition to peak values, I track how coherence evolves across time, trials, and experimental conditions. The goal is to quantify how neural communication changes under control, scopolamine, and scopolamine plus nerve-block conditions, and whether specific frequency bands (e.g. theta) show distinct patterns. This involves extracting coherence trajectories, comparing them across texture plates and region pairs, and identifying condition-dependent disruptions in rhythmic coupling.
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