About me
I enjoy connecting the pieces: reliable data, useful models, and tools people can actually use.
I’m a master’s student in Data Science at Columbia University. My work spans data engineering, machine learning, and cloud infrastructure, and I enjoy projects where those areas come together.
During my summer internship at VeYou, I built data pipelines and a BigQuery warehouse that supported dashboards and analytics for internal teams and partners. At Columbia, my work has included clinical text de-identification at CUIMC and financial data analysis at the Weatherhead East Asian Institute.
Before Columbia, I worked at Cognizant supporting production workloads on Google Cloud. That experience shaped how I approach engineering: understand what went wrong, make the fix repeatable, and make the system easier to maintain.
Alongside my studies, I’m a teaching assistant for Cloud Computing and Introduction to Databases. My projects follow a common thread: making the evidence behind an answer visible, whether that is a cricket tool call, a clinical text span, or a data-quality check.
How I work
- Understand the problem before choosing the tools.
- Check data quality and evaluate results.
- Build systems that are easy to inspect and maintain.
- Explain decisions clearly to the people using the work.
Quick facts
- Location
- New York, NY
- Program
- Columbia MSDS, exp. Dec 2026
- Latest role
- VeYou · Summer Data Engineer Intern (completed)
- Teaching
- TA · Cloud Computing + Intro to Databases
- Seeking
- AI/ML/DS/Data Engineering roles
- Stack
- Python · SQL · BigQuery · PyTorch