Projects

Bayesian analysis of COVID-19 Vaccine Efficacy

Bayesian Statistics

Bayesian modeling and analysis to evaluate COVID-19 vaccine effectiveness using statistical inference methods.

R · Bayesian analysis statistical modeling

Neural coherence in texture exploration

Neuroscience

Longitudinal analysis of coherence across M1U, S1U, and PFU during an orofacial texture task in rhesus macaques, comparing control, scopolamine, and nerve block conditions.

MATLAB · statistics signal processing

R-Shiny dashboard (in progress)

Data Visualization

Shiny dashboard exploring vaccine coverage (DTP3, MCV1, BCG) against health expenditure trends, with clustering to surface regional and income-based patterns over time.

R-Shiny · UMAP · HDBSCAN

Experience

Data Science Intern

June 2025 – Aug 2025

Fred Hutchinson Cancer Research Center

Undergraduate Researcher

Oct 2023 – Present

University of Washington (Arce-McShane Group)

Teaching Assistant

March 2025 – June 2025

University of Washington

Behavioral Data Research Assistant

Oct 2023 – Nov 2024

Washington National Primate Center

Research

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.