I apply data science methods to investigate social and economic systems. I have utilized Computational Social Science and Data Analytics to investigate algorithmic fairness, digital economics, and information asymmetry.
My goal is to bridge the gap between technical data science and social inquiry, applying methods like OLS Regression, Bias Auditing, and Causal Inference to understand how information systems impact society.
🎯 Open to research collaborations and academic opportunities.
How Do Automated Decision Systems Penalize Marginalized Groups, & Can We Fix It Without Breaking The Model?
Auditing a fintech credit scoring model for gender bias using Python and Fairlearn. Investigating FAccT principles in information systems. I replicated a credit scoring pipeline and applied Microsoft’s Fairlearn toolkit to test ‘Demographic Parity’ constraints.
Social Media Sentiment & Topic Extraction
A Computational Text Analysis project using Python, VADER, and Gensim. Built an automated pipeline to clean unstructured social media comments, quantify public sentiment, and extract latent discourse topics using Latent Dirichlet Allocation (LDA).
The Airbnb Effect: NYC Housing Markets
A Computational Social Science project using Python and OLS Regression to quantify how short-term rental density impacts local housing prices across 164 NYC neighborhoods.
SQL Data Analysis: Chinook Music Store
Exploratory data analysis on a music store’s sales and customer behavior using SQL queries.
Customer-Personality Analysis in Tableau
Built an interactive dashboard for visualizing customer segmentation data.
Google Sheet Automation
Streamlined manual workflows using formulas and conditional formatting in Google Sheets.