🎓 CS Graduate from UC Irvine, current Software Engineer at Prism Software, and M.S. Data Science student at UC Berkeley.
🌱 Exploring interests in AI/ML, full-stack development, and data analytics.
🛠️ Skills
- Languages & Frameworks: Python, JavaScript (React, Angular, TypeScript, Node), HTML, CSS, SQL, R
- Development Tools: AWS, Azure DevOps, TeamCity, Git, Docker
👩💻 I’m actively looking for full-time roles in Software Engineering, Frontend Development, and Data Analysis/Engineering. I’m passionate about working on impactful projects that combine data-driven decision-making with technology and design to solve real-world problems. If you’re interested in collaborating, please feel free to connect with me via the following platforms!
This project is a simple and intuitive tool designed for plant lovers to easily manage and care for their plants. With this app, users can store plant details like names, species, and photos, while keeping track of care activities such as watering and repotting. It offers the convenience of setting reminders to stay on top of watering schedules and logs plant care actions. A project in the works :)
Insul-in is a mobile game developed as part of project GLUKI to educate young children with Type 1 Diabetes on the process of administering insulin. The game takes on a "brick breaker"-style format, making learning about insulin administration fun and interactive. The app aims to teach children the importance of managing their condition through gameplay, combining education with entertainment to help reinforce key concepts. This project was developed by a team of 5 using Unity Canvas, with design mock-ups created in Figma. As the UI artist, I designed the in-game user interface, including the HUD and other creative components.
This project investigates how the percentage of time spent using electronic technologies at work varies by age. The analysis explores age-related disparities in technology usage across different job types and industries. By examining the relationship between age, work hours, and technology adoption while controlling for industry and occupation, the study aims to provide insights into workforce development and inform policies on technology training and support for diverse age groups in professional environments.
This project models stocks as a correlation network to uncover hidden dependencies that traditional sector-based diversification overlooks. Using the Louvain algorithm to detect clusters of correlated assets and PageRank to identify influential stocks, it reveals cross-sector relationships and systemic risks not visible in standard models.
This project analyzes partisan disparities in primary and secondary education policy preferences between Democratic and Republican voters using data from the ANES 2024 Pilot Study. It examines attitudes toward education, including views on college professors and perceptions of party handling public school curricula. The goal is to uncover differences in how each party approaches education policy and understand the impact of political affiliation on educational priorities.
This project analyzes the relationship between various global socioeconomic indicators, including government expenditure on education and GDP per capita, across three regions: Western Europe, Western Africa, and South Asia. Using a dataset from World Development Indicators (2000-2023), the study explores how education spending and economic development correlate within different regions, revealing nuanced insights into the factors that drive economic growth and educational disparities. Statistical methods such as clustering and influence analysis were applied to uncover hidden dependencies and cross-sector relationships not typically visible in standard models.