Hi, I'm Sean Yoon
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I am a Full Stack Developer with a passion for creating dynamic and responsive web applications. I have experience in both front-end and back-end development in both educational and professional settings. Of course, I am always eager to learn new technologies and improve my craft.

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Technologies

I have worked with a range of technologies in the web development world, from both front-end to back-end development.


  • React

    Experience with JavaScript, TypeScript, and Component libraries such as DevExtreme


  • ASP.NET

    Experience with C#, ASP.NET, ASP.NET Web API, and Entity Framework


  • Node.js

    Experience with Node.js Express, Mongoose, and PostgreSQL


  • SQL

    Experience with Microsoft SQL Server, Oracle Database, and Snowflake Data Cloud


  • Python

    Experience with Jupyter, PyTorch, Scikit-learn, NumPy, and Pandas


  • Git

    Experience with GitHub, Azure DevOps, Azure DevOps Server (TFS)

Technical Experience

View my technical work experience below!

Projects

Here are some of my projects that I have worked on. I have included a brief description of each project, as well as the technologies used.

Uber Eats vs. Door Dash


Ever wonder which platform has better deals? This application utilizes web scraping to extract data from Uber Eats and Door Dash. It is still under construction - be sure to check back later!

  • React.js
  • ASP.NET
  • SQL Server

Ace Detailing Web Application


The business website for Ace Detailing, this website provides a beautiful and dynamic front page, blog, as well as a booking system that allows users to connect with the business owners and view status updates.

  • React.js
  • Node.js Express
  • MongoDB
  • GCP

SFUnited Web Application


This application combines SFU course scheduling, Rate My Professor, SFU clubs, and Google Maps API to create a centralized hub for students to access common resources in one location.

  • HTML
  • CSS
  • Node.js Express
  • PostgreSQL

Phishing Detector


Utilizing Random Forest Models, this application is able to distinguish between phishing and non-phishing emails with a 98% accuracy rate. Model validation was done using ROC-AUC.

  • Python
  • Scikit-learn
  • Pytorch

Contact Me!

Please do not hesitate to reach out 🙂