REVIEW CATEGORIZATION FOR MUSEUMS
September 2020 - Present
This project is for the Vertically Integrated Project: Art & AI. I am currently in the process of creating a sentiment analysis model using unsupervised machine learning to categorize museum reviews pulled from TripAdvisor as "positive," "negative," or "neutral." Moving forward, I would like to expand this project to detect emotional states.
GitHub • VIP: Art & AI
A project created for HackGT 7 2020 that seeks to solve the problem of biased news increasingly appearing at the top of newsfeeds. This project uses a sentiment analysis model to detect for bias in articles. The results from this model are then used to rank the news articles in a filtering system that accounts for bias as well as reliability and recency. Got bias? won 3rd place from NewsQ.
GitHub • got bias? website
UC BERKERLEY'S PACMAN AI PROJECT
Implemented search algorithms (BFS, DFS, UCS, and A*) and two consistent, admissable heuristics to help the agent successfuly find all corners of the maze and eat all fruits. Used reinforcement learning (Q-learning and Value Iteration) to train the agent to solve the maze.
UC Berkeley's Pacman AI Project
I'm a 2nd year CS major with threads in Intelligence and People and a minor in Linguistics at Georgia Tech. Recently, I have been focusing on NLP and computational linguistics. More specifically, I have been working on projects involving both supervised and unsupervised machine learning to conduct sentiment analysis. I'm especially interested in working with back-end development and ML/ AI. Here at Tech, I'm on the content team for NAR Magazine and a member of GT Filmmakers.