Research, analytics, and technical project work
I work on analytics, publication support, and technical projects with an emphasis on reliable workflows, readable outputs, and useful follow-through.
Quick introduction
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, 4.0 GPA
Peer-reviewed work
8 publications
Direct co-authorship plus broader study-group publication support
A tighter set of work I want front and center: ViewerAtlas, direct co-authored papers, and research-heavy technical analysis.
Featured GitHub project
Built a Twitch audience-mapping pipeline that collects audience-presence data, models shared-viewer overlap, and visualizes community structure.
ViewerAtlas brings together collection, storage, analysis, and visualization in one workflow. It is the strongest software example on the site of how I like to work: make the pipeline dependable first, then make the output easy to interpret.
Direct co-author paper
Direct co-author on a Journal of Clinical Investigation paper on broadly protective antibody responses across distinct Omicron subvariants.
I wanted the site to foreground papers where I am listed directly in the author line. This one reflects collaborative work at the intersection of immunology, experimental output, and publication support.
Direct co-author paper
Direct co-author on a Vaccine paper examining durable humoral responses in solid organ transplant recipients with and without HIV.
This is another paper where I am named directly as a co-author, and it highlights the kind of careful, team-based research work I want featured more prominently on the site.
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.