About Me
Hi, I am Russell Chao. I am currently an undergraduate at Rensselaer Polytechnic Institute (RPI),
expected to receive my Bachelor's of Computer Science in May 2026.
I am passionate about building scalable software to solve real-world problems. I like to specialize in Backend,
Full-Stack Development, Data Analysis, and Machine Learning. I have built projects that have ranged from an NFL Playcall Recommender
to an AI-Powered Journaling Web App.
I am proficient in languages like Python, Java, JavaScript, and SQL. I have experience working with frameworks and libraries
such as React, Node.js, Express, Flask, Spring, Pandas, and Sci-kit Learn. I also have experience working with databases like PostgreSQL, and MongoDB,
and cloud services like Vercel and Render. I am always looking to expand my skillset and learn new technologies.
I am from Long Island, New York. I grew up with my parents and older sister. Outside of work and academics,
I like to watch NFL Football, hit the gym, and go out for walks and runs.
Projects
DigitalOC
This is a Machine Learning model that is trained on historic NFL play-by-play data and team playcalling tendencies
to predict the most optimal play in any situation in an NFL game. This project also features a
web-based component that will allow users to enter a game situation to the interface.
Languages: Python, JavaScript, HTML, CSS
Frameworks: Flask, React
Libraries: Pandas, Scikit-Learn, Matplotlib
Deployment: TBD
RetroJournal
This is a Journaling Web App that combines Nostalgic Aesthetics with Modern AI-powered insights.
Take a break from the draining effects of modern social media, doomscrolling and write down your
thoughts and feelings in a Retro-Themed Environment while also recieving AI-powered advice. Made at HackRPI 2025.
Languages: JavaScript, HTML, CSS,
Frameworks: Node.js, Express, React, MongoDB, Clerk for Auth, OpenAI API
Libraries: Chart.js, Tailwind CSS
Deployment: Vercel, Render
NFLP
This Web App leverages NLP to analyze and summarize NFL news articles,
providing users with the overall sentiment and toxicity of the content, along with extracted players and teams.
This project was purely experimental to explore the capabilities of NLP in the sports domain.
Languages: Python, Java, JavaScript, HTML, CSS
Frameworks: Flask, Spring Boot, React, PostgreSQL
Libraries: NLTK, SpaCy
Deployment: AWS Amplify, Docker