| Lauren Hyde

I am a backend developer for a platform built by Prof. Wade Fagen-Ulmschneider and other members of the d7 Research Group to help students learn data science in a beginner-friendly way. The site has already been used by thousands of Illinois students in exams and homework problems. I developed an API for running and grading student python code in real time and refactored other components of the backend architecture to improve scalability. I also contributed to new features for students and staff including multi-course support, new assessment types, and platform analytics. Our tech stack includes Node.js, Express.js, and Sequelize.js in TypeScript with Jest for testing and HTMX for front-end interactivity.
View our work here!

Collaboration between Hack4Impact UIUC and OpenAQ to deliver an educational webpage for browsing air quality and AQI calculation information. I developed several different pages and animations to contribute to the welcome & education flow of the website and improve user experience. Our tech stack includes Solid.js with TSX, SCSS, and MotionOne animation in order to seamlessly integrate it with OpenAQ's existing webpage.
Check out H4I's GitHub for our other work!

MicroCredentials@Illinois is a webpage built by the d7 Research Group to issue LinkedIn credentials to students in Illinois coursework, recognizing their academic achievements. It is currently already used by several UIUC courses, and our work focuses on expanding the platform to scale to many different classes and a better user experience for instructors. To accommodate this, we automated several different stages of the badge issuing process using Express.js, Node.js, and HTML/HBS. Our work was presented at the 2024 Illinois Undergraduate Research Symposium.
Learn more about this project here

This project is a collaboration between Hack4Impact UIUC and international nonprofit Inclusive STEM aiming to improve accessibility in STEM education. We are creating a mechanism for organizations to upload accessible resources so that those who need them can downlaod them whenever they need to, as well as the corresponding authentication and book request mechanisms for organizations and users. As a full-stack developer on this project, I created the end-to-end organization verification page and various other components using Node.js, Express.js, React, and MongoDB with Chakra UI for accessible components.
Check out H4I's other projects!

PickMe is a full-stack web application designed to help users simplify the decision of where to eat. Our tech stack includes React.js with TypeScript on the frontend, and Flask on the backend, with a MongoDB database and Google OAuth for user management. Once logged in, users can indicate and save their preferences for certain cuisines using dynamic sliders, and these are taken into account for the restaurant recommendations. For each decision, we use the Geolocate API and Google Places API to find nearby restaurants that match the user's cuisine preferences, using KMeans clustering to find the most highly regarded ones. These are then displayed on the frontend using custom React.js components and the user can indicate their preference (like or dislike) for each option. We then select the best option from the liked options and store this in the database for potential later reference by the user.
Check out our GitHub!

SpotifAI is a website I designed as a personal project to display Spotify's listening history statistics from a user's account. For any user, the website will display the user's playlists (along with the author and number of songs) and their respective top 3 artists, as well as the user's 50 top artists, songs, and recently played songs. The website can also play "preview" clips of the user's top 3 songs. All of this data is available for multiple time periods, and the user can navigate between these periods using a dropdown menu. I created this app using HTML, CSS, and Javascript with the Spotify API.
View the GitHub Repo

I attended the Wharton Data Science Academy in the summer of 2022. It was an intensive, 3-week course on data science and statistical analysis fundamentals in R. Our final project was an analysis of the factors that contribute to depression and other mental health concerns in college students. We investigated a total of 50 qualitative and quantitative variables, around 10 of which ended up being significant in our final models. Our techniques included logistic and linear regressions, a random forest model, and a neural network, as well as various data visualizations to understand our findings. Our research was presented at the cumulative Data Science Academy Symposium.

StoryStream is a personal project I created as a solution to difficult-to-read word and txt files. StoryStream incorporates a colorful and intuitive interface, automatic bookmarking, and divides the text up into pages and paragraphs to improve attention span. The app also supports multiple users at once, and features a web scraper made with BeautifulSoup, so that it can display hard-to-read webpages as well. StoryStream was created with HTML, CSS, Python (Flask), and client-side JS.
View the GitHub Repo