My group won Best Sustainability Hack for our project Chilladelphia, an app that helps cool down Philadelphia by providing personalized “chill ratings” based on greenery analysis and eco-friendly suggestions. We used computer vision to analyze satellite images of Philadelphia for tree coverage. I built the frontend using Vite + React and TailwindCSS, ensuring smooth navigation with React Router and Radix UI for theming. I incorporated PropelAuth for user authentication and used the Radar SDK for address autocomplete and map display. On the backend, I set up the MongoDB database connection and implemented routing with Axios API requests to fetch data from the backend to frontend.

Here is the project: https://github.com/carolinecummings19/Chilladelphia

And here is the DevPost submission: https://devpost.com/software/chilladelphia?ref_content=user-portfolio&ref_feature=in_progress

I collaborated with a three other classmates to build a social media app that resembled Instagram, which we called Instagang. My primary responsibilities included developing the database query endpoints and establishing connections between the frontend and backend using Axios routes. I also implemented key routing features such as login, signup, and the home feed, which supported likes, comments, and post rankings. Additionally, I connected frontend to backend for the search functionality for posts and users, the friends page to fetch current friends, recommendations, and requests, as well as the profile page where users could update their information like passwords and emails.

As part of a group of four, I built a UNIX-like operating system in C, which included a basic priority scheduler, a FAT file system, and user shell interactions. My primary contributions focused on implementing the threading and scheduling processes for the priority scheduler and ensuring efficient task management. Additionally, I handled job control and defined built-in commands (like sleep, ps, kill, echo, cat, etc seen to the left) for user interactions within the shell, facilitating communication between the user and the system.

In this class, I explored techniques for processing and analyzing large datasets using Pandas and SQL. For my final project, I worked with 2 other classmates to build predictive models, including linear regression, gradient boosting regression, and random forest, using datasets of weather conditions and marathon finishing times for the Berlin Marathon. Our goal was to analyze and predict how environmental factors affect marathon race times, providing valuable insights into the impact of weather on athletic performance.

I applied agile methodologies throughout a semester-long project where I worked in a group of five to develop an app designed to promote sustainability habits. The app encouraged users to complete eco-friendly tasks and set reminders, with the goal of fostering long-term sustainable behaviors. We built the application using the MERN Stack (MongoDB, Express.js, React, and Node.js), ensuring smooth data management, dynamic user interaction, and efficient backend processes. By following agile practices, we maintained consistent collaboration and iterative development to enhance the app’s functionality over time.