Computer Science @ Purdue 26
Shreya Komarabattini
Software Engineer building full-stack & NLP applications.
My work spans full-stack apps, data visualization research, machine learning, and human-centered interfaces.
Moodle
Online drawing and guessing game you can play with friends. One person draws a secret word while the others try to guess it in the chat. You can make a room, invite others with a code, play against computer players, and even draw using hand gestures with your camera.
Protoplay
Work in ProgressInteractive platform that teaches engineering concepts through gamified simulations, architecture challenges, and algorithm puzzles by learning and building, not memorising.
Traffic Sign Recognition
Senior Capstone team project focused on developing a mobile application that detects and classifies traffic signs in real time using a trained deep learning model. The app enhances driver safety by delivering fast and accurate traffic sign recognition through a live camera feed.
HealthyCal
Full-stack nutrition tracker — React frontend, Node/Express API, MongoDB storage. Helps users stay aware of how different foods contribute to daily energy and nutrient goals.
MEMETIME
An interactive museum tracing internet culture from 1996 to today. Visitors descend through 12 immersive environments, one per era: gallery vitrines, a meme genealogy lab built with React Flow, a viral spread simulator, a popularity landscape, and a pixel-art culture skyline. Every scene owns its own lighting, atmosphere, and motion language.
IT Ticket Routing Automation
End-to-end ML web app that reads free-text Helpdesk tickets and routes them to the right IT support group, predicting support group, issue type, and priority across 8 IT groups. Combines a trained NLP pipeline, a FastAPI backend, and a React dashboard with confidence scores and analytics.
FW Crime Analysis
Classified 150,000+ crime records using Python, identified violent vs. non-violent patterns, mapped geographic hotspots, and surfaced temporal trends for data-driven municipal insights.
Categorical Data Visualization Study
Investigated how users interpret bar, line, and stacked bar charts. Designed user studies to evaluate visualization clarity, and formulated evidence-based guidelines for better data comprehension. Presented at Purdue's Annual Research Symposium.