Welcome to my GitHub! I'm a graduate student in Computational Analysis and Public Policy @ the University of Chicago. I am interested in blending data science with public policy to drive social change, focusing on areas like climate action, hunger relief, and equitable urban development!

• 🌱 As a student, I’m deepening my expertise in machine learning, data management, and the practical application of data science to policy and social change.
• 💬 Ask me about my experiences with implementing data-driven solutions in public policy, my advocacy work, or anything related to the intersection of technology and social good.

Check out my resume!

Want to chat? Connect with me on Linkedin or by email!

• Climate Conversations (2024): This project leverages Reddit discussions and academic research to refine climate change communication for policymakers, creating a toolkit that aligns messaging with public concerns and scientific evidence through topic modeling and moral foundations theory. It simplifies complex scientific findings into concise summaries, using NLP and moral foundations analysis to overcome challenges in climate policy communication and enhance policy formulation. Read more here & View our final presentation

• Portland Rank Choice Voting Simulation (2024): In collaboration with Moon Duchin and the MGGG Redistricting Lab, this study investigates the influence of Portland's new proportional rank choice voting system on its 2024 city council elections, using simulations to analyze voter behavior across different demographic and ideological groups. Utilizing MGGG's Votekit Package, it offers strategic insights for campaigns and the election of POC candidates. (Codebase not publically available)

• Postgrad Job Search (2024) [IN PROGRESS]: Adapted from project initially designed by necabotheking. Scraper to find relevant job listings and deliver in a daily email using Github Automation.

• Pedal Pals (2023): Study on Divvy bike ridership between 2014 and 2019, analyzing one million trips, uncovered notable shifts in trip durations, especially during summer months. Using models like KNN, Random Forest, and MLP, we found MLP to be most effective in forecasting trip durations, highlighting the importance of accounting for temporal data changes in predictive modeling. Read more here

• Watts Up CA (2023): This project delves into California's electricity consumption and forecasting from January 1990 to June 2023, underscoring the challenge of high overall but low per capita energy use as the state aims for 100% renewable energy by 2040. Utilizing time series analysis, the team explored how weather, demand-supply dynamics, and policy shifts affect energy trends, revealing key predictors and the forecasting complexity due to evolving trends and multiple seasonalities. View our final presentation here

• Deconflict Committee Scheduler Data Extraction, Database Creation, and Analysis (2023): As a Data Science fellow for the U.S. House of Representatives Digital Service, I contributed to the creation of a backend for the Committee Scheduler Deconflict tool, introducing data extraction, storage, analysis, and reporting features. (Codebase not publically available) This work was completed as part of the Congressional Modernization Fellowship program.

• Costa Rican Poverty Prediction (2023): Develop machine learning models to predict poverty levels in Costa Rican households, employing techniques like oversampling and logistic regression for enhanced accuracy amid data imbalances. We also explore binary poverty categorization and discuss the model's limitations and potential areas for improvement. Read more here

• Sentiment of Local Newspaper Coverage on the 2023 Primary Election's Mayoral Candidates in Chicago (2023): Examining the Scope and Sentiment of Local Newspaper Coverage on the 2023 Primary Election's Mayoral Candidates in Chicago. Read more here