I am an Economics and Computer Science & Software Engineering double major at the University of Washington, expecting to graduate in June 2027 with a 3.77 GPA. I am passionate about using programming, economics, and financial mathematics to craft data-informed decisions for businesses, hospitals, government agencies, and nonprofit organizations.
October 2025 - Current
Trickfire Robotics (Student Organization)
Developed autonomous navigation algorithms using Python, computer vision, and LiDAR data to enable real-time obstacle detection and path planning for a competition mars rover. Used OpenCV and ROS frameworks to process sensor input, improving object accuracy. Leveraged AI-powered solutions like Google Gemini to automate debugging and code generation, enhancing productivity by 30%.
June 2025 - October 2025
The House of Wisdom
Led the development of a full-stack web app for 100+ users. Engineered ETL pipelines to track attendance for 300+ students and log hours for 50+ employees. Built dynamic dashboards with Python, Firebase, and Google Data Studio, enabling data-driven decisions that led to a 40% expansion of the organization's reach and increased administrative efficiency by over 50%.
Analyzed the disparity between the U.S. minimum wage and cost of living by modeling how wage trends would appear if indexed to inflation, using PostgreSQL and BLS data. Developed Power BI dashboards visualizing long-term affordability changes for housing, offering clear insights into economic trends.
Download Report (.pbix) →Optimized a stock portfolio by analyzing a decade of historical data from Yahoo Finance. Ran 50,000 Monte Carlo simulations to find optimal allocations. Generated Seaborn visualizations of the efficient frontier, highlighting the benefits of diversification and portfolios with the best risk-return profiles.
View on GitHub →Used EV charging data, OpenStreetMap geometry, and machine learning to identify charging deserts and optimal spots for expansion. Processed large datasets with Python and PostgreSQL (PostGIS), then applied XGBoost and Prophet models to predict demand and create Dash visualizations.
View on GitHub →