Georges Gewargis

Hi, I'm Georges Gewargis

Computer Science Student & Web Developer

I am currently learning and creating beautiful, responsive websites with smooth animations and modern designs. Studying at Washington University in St. Louis, I am passionate about technology and CS as a whole.

About Me

Who am I?

I'm a passionate Computer Science student with a love for full-stack development. I've built several personal and business projects using a wide range of modern technologies and frameworks.

Outside of coding, I enjoy playing video games, listening to music, skateboarding, playing basketball, going to the gym, and spending time with friends.

Location

St. Louis, MO | Chicago, IL

Email

g.georges@wustl.edu

Georges Gewargis

What I Do

Web Development

Creating responsive websites using React, Next.js, and modern CSS. Strong focus on performance and clean code.

UI/UX Design

Designing intuitive and beautiful user interfaces with attention to detail and user experience.

Frontend Engineering

Building interactive, animated interfaces with modern technologies.

Programming Languages

My Education

Washington University in St. Louis

Washington University in St. Louis

Bachelor of Science in Computer Science

2024 - 2028

Relevant Coursework: Data Structures & Algorithms, Rapid Prototype Development & Creative Programming, Web Development, Discrete Math, Calculus 3

My Experience

Student Life (Studlife Newspaper)

Web Development Lead at Student Life (Studlife Newspaper)

Jan 2026 – Present

• Manage and maintain the newspaper’s website (WordPress, custom PHP) • Lead UI redesign to modernize layout and improve UX • Implement updates and troubleshoot backend for smooth publishing

www.studlife.com

JukeHouse Music Publishing

Co-Founder & Lead Developer at JukeHouse Music Publishing

July 2025 - Present

• Lead full-stack development of JukeHouse.fm (Django, PostgreSQL) • Built RESTful APIs for royalty tracking and songwriter management • Integrated with global music organizations for automated royalty collection

jukehouse.fm

Habitat Financial

Software Development Intern at Habitat Financial

June 2025 - August 2025

• Developed full-stack features for royalty processing (Django, PostgreSQL, HTMX) • Built responsive interfaces for artist and revenue management • Implemented data pipelines for complex royalty calculations • Contributed to API integration with music platforms and payment systems

www.habitat.financial

Bliss Salon of Glenview

Web Developer at Bliss Salon of Glenview

April 2025 - Present

• Designed and built responsive salon website (React, Vite, SCSS) • Optimized SEO with sitemap, robots.txt, and structured data • Integrated custom DNS/SSL for secure deployment

blissglenview.com

Featured Projects

Trellofy screenshot

Trellofy

A full-stack Trello clone built with React, Express.js, Node.js, and MongoDB. Features drag-and-drop functionality, user authentication, and responsive design for seamless task management. Served on AWS EC2 (expired due to free-tier limits) with a MongoDB Atlas database.

ReactExpress.jsNode.jsMongoDBAWS EC2
MovieFinder screenshot

MovieFinder

A dynamic movie search application utilizing the TMDb API to fetch and display movie data. Emphasis of the project was to build with React and create a polished, detailed UI. Deployed on GitHub Pages.

ReactTMDb APIFrontend Design
Multi Room Chat Application screenshot

Multi Room Chat Application

A real-time multi-room chat application built with Node.js, and Socket.io. Features user authentication, room creation, and message persistence. Deployed on AWS EC2 (expired due to free-tier limits).

Node.jsSocket.ioAWS EC2
NutriScan screenshot

NutriScan

Advanced Streamlit application leveraging image analysis to assess nutritional content of food. Implemented API integrations with OpenAI and Foodvisor for personalized recipe suggestions and daily caloric intake calculations.

PythonStreamlitOpenAI APIFoodvisor API
ML Recidivism Predictor screenshot

ML Recidivism Predictor

Designed and implemented a fair and ethical machine learning model to predict recidivation rates with 80% accuracy using Florida county jail data. Analyzed 11,000-row dataset using Pandas, Scikit-learn neural networks, and NumPy.

PythonPandasScikit-learnNumPy

Get In Touch

Contact Information

Feel free to reach out to me for collaborations, job opportunities, or just to say hello!

Send Me a Message