I work on analytics, publication support, and technical projects with an emphasis on reliable workflows, readable outputs, and useful follow-through.
I am a New York-based data analyst with experience across research operations, clinical data work, publication support, and graduate study in data science. I like work that takes complicated information and turns it into something dependable, readable, and genuinely useful.
Research analytics
2+ years
Data analysis and study support at Mount Sinai
Graduate study
M.S. Data Science
Pace University, 3.96 GPA
Peer-reviewed work
9 publications
Direct co-authorship plus broader study-group publication support
Featured GitHub project · 2025-Present
Maps Twitch communities through shared audience presence: sampled chat participation becomes a weighted channel-overlap graph, Louvain detects the communities, and a React explorer makes the structure readable.
Direct co-author paper · 2026
Direct co-author on a Vaccine paper examining durable humoral SARS-CoV-2 responses in solid organ transplant recipients with and without HIV.
Featured GitHub project · 2026
A local-first desktop planner where a plain-language request becomes a typed, twice-validated schedule proposal you review before anything is written to the database.
The goal is not just clean analysis. It is making sure the output is understandable, reliable, and useful once it leaves the notebook.
I like taking messy source data and turning it into work that is reproducible, explainable, and easy for other people to use.
A strong analysis only goes so far if the figure, writeup, or handoff does not make sense to the next person in the room.
I am comfortable moving between analysis, documentation, dashboards, and project support when that is what helps the work move forward.