• Lead statistical analyses on large-scale health and claims datasets, informing national infectious disease surveillance strategies. • Designed reproducible workflows for algorithmic identification of pregnancy cases across multi-cloud platforms. • Developed scalable geospatial pipelines and metrics for pattern detection across ZIP, county, and state levels. • Created visual analytics dashboards and reports that guided cross-functional decision-making. • Mentored scientists and analysts on experimentation design, modern analytics tooling, and cloud platform adoption. • Partnered with data governance, product owners, and policy leads to ensure compliance and interpretability. • Created interactive, population-adjusted visualizations with dynamic binning to support data-driven decision-making across teams. • Supported data product clearance processes, ensuring accuracy, reproducibility, and regulatory compliance for internal and external communication.

Centers for Disease Control and Prevention | May 2024 - May 2025

CDC 2024 Office of Readiness and Response Fellow

• Conducted a systematic review on emergency preparedness data quality, using Python and visualization tools to deliver insights that shaped strategic decision-making. • Developed Python scripts in Jupyter Notebook and Spyder to analyze relationships between COVID-19 case data, social vulnerability, social determinants of health and health equity across U.S. regions. • Built visualizations and interactive maps (e.g., choropleths, time-series) to highlight trends in COVID-19 deaths by sex and age, improving understanding of population-level disparities. • Created geospatial maps in Python for active CDC emergency responses including outbreaks, natural disasters, and other crises, aiding situational awareness and coordination. • Produced interactive data products including maps, dashboards, and time-series analyses used in CDC briefings.

Georgia State University | Aug 2012 - Jun 2023

Graduate Research and Teaching Assistant

• Led computational modeling projects using Unix-based HPC clusters and machine learning workflows. • Applied scientific methodologies to simulation design and statistical data interpretation in biophysical chemistry. • Maintained organized data repositories and trained undergraduates in coding, simulation, and data analysis. • Developed reproducible workflows using Shell scripting, Python, and R for data-intensive research.

University of Washington eScience Institute | Jun 2021 - Aug 2021

Data Science for Social Good (DSSG) Intern

• Designed and implemented MCMC-based redistricting simulations using GerryChain (Python) to evaluate partisan bias and district compactness. • Generated interactive maps and reports visualizing fair redistricting outcomes; findings presented to civic organizations and policy leaders. • Co-authored a user guide to support public engagement and reproducibility in redistricting research; shared via open-source platforms. • Employed GitHub and Jupyter Notebooks to manage version control, reproducibility, maintain documentation, and ensure research transparency.

My Education

Take a look at my formal education

Doctor of Philosophy (Ph.D.) in Computational Biophysical ChemistryGeorgia State University | 2022

Master of Science (M.S.) in Computational Biophysical ChemistryGeorgia State University | 2016

Bachelor of Science (B.S.) in General ChemistryGeorgia State University | 2012

About Me

PhD data scientist with 10+ years' experience in analytics, machine learning & visualization. Skilled in Python, R & SQL. Proven record in healthcare, public health, and geospatial analysis at CDC and academic settings.

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