What I've done
I've worked on numerous interesting coding projects over the past couple years which have given me valuable experience in various different niches within computer science. From app development to machine learning, to full-stack web development and even AI APIs, I've gained numerous skillsets that have helped me greatly thanks to these projects. You can see more of the things that I've worked on on my GitHub page.
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Space Company Stock Predictor - HackRPI Hackathon
At a hackathon at my college in fall 2022, me and my team built a Python program that predicts the future of space companies' stocks using machine learning on Google Search Trends. We used HTML and CSS for the front-end, and used NumPy and Pandas for the data extraction and scrubbing. I really developed my leadership skills by spearheading the development of this project in less than 24 hours, and we ended up winning an award at a hackathon -- The Best Data Science Hack Award!
Discord Clone w/ Real-Time Chat and Video
Spearheaded the development of a full-stack Discord clone where users can create servers, invite friends to them with working invite links, and can conduct real-time video and audio calls using WebRTC technology. Uses AWS Lambda, WebSocket service, and DynamoDB for the backend API, and React and TailwindCSS for front end.
Brain Tumor Classification with Neural Networks
Built an end-to-end pipeline with 10,000 rows of U.S. Bank Customer data from Kaggle to predict how likely a customer is to churn, training 5 different machine learning models (Random Forest, XGBoost, Catering to Neighbors, etc.) and using Llama 3.1 to make these predictions. Worked with team of 3 members and was mentored by professional software engineer throughout development.
Automated Financial Analysis App using LLMs
This is my favorite project that I've ever made. Here, I used RAG on a financial dataset, web scraping, and sentiment extraction from news articles to generate quick stock reports so users don't have to spend hours researching stocks to no avail. Users can enter what kinds of stocks their looking for, and the program will use RAG to find the best stocks in their desired category, along with modeling each stock's normalized stock price history and providing an AI-generated stock comparison summary. Also contains sentiment analysis bar charts for each stock as well as a radar for modeling stock health and growth potential.
Contact Me
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