Student Information
Graduate Student
Software Engineering
Ira A Fulton Engineering
Long Bio
Hi, I’m Smit, a software engineer in the making, and a “build-it-until-it-works” kind of developer by instinct. I’m currently pursuing my Master’s in Software Engineering at Arizona State University and I love working on backend systems, AI-powered apps, and anything that combines logic with a bit of chaos.
My projects range from developing a full-stack Software Metrics Calculator using Java Spring Boot and Vue.js to an AI-driven waste classification tool (RecycleMate), which was published in IEEE and classifies trash with 85% accuracy. I’ve also built a carpooling web platform from scratch and engineered a recommendation system for short videos using NLP techniques like CountVectorizer and Cosine Similarity (also IEEE-published). That one proves I can make Python and Firebase agree on something.
Professionally, I work at the ASU Experience Center where I help customers solve technical problems and navigate digital services.
I’ve also had the thrill of winning the Smart India Hackathon 2023, where my team uncovered 60+ vulnerabilities in OpenSSL-based systems and presented the findings directly to company leadership.
Tech stack? I speak Python, Java, C++, React, Flask, Kubernetes, GitHub Actions, AWS and sarcasm, fluently.
In short, I enjoy turning real-world problems into clean, scalable code. And if I can automate something in the process? Even better.
Education
- I’m currently pursuing a Master’s in Software Engineering at Arizona State University, expected to graduate in May 2026.
- I hold a Bachelor's degree in Computer Engineering with Honors in AI & ML from the University of Mumbai.
Publications
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NLP-Powered Short Video Enrichment System
This research paper presents a smart short video recommendation system powered by Natural Language Processing (NLP). By using techniques like count vectorization and cosine similarity, the system analyzes video hashtags, descriptions, and user IDs to suggest the top five most relevant reels. Built with React, Firebase, and Flask, the solution aims to connect users quickly to content they’ll enjoy, making the recommendation process both efficient and personalized.
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Recyclable and Non-Recyclable Waste Recognition using Image Processing with Machine Learning: Recycle Mate
This research paper introduces "Recycle Mate," a machine learning-based system for recognizing recyclable and non-recyclable waste using image processing. By leveraging a Convolutional Neural Network (CNN) built with Keras and integrated via Django, the model efficiently classifies waste items from images into two categories. The system aims to simplify waste sorting for users, improve recycling accuracy, and support smarter, more sustainable waste management practices.
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Bhagavad Geeta Based ChatBot
This paper presents a chatbot inspired by the Bhagavad Geeta, designed to answer questions about life, history, and philosophy. Built using the OpenAI API and advanced language models like GPT-2 and GPT-3, the bot interprets user queries and provides thoughtful, context-aware responses based on the teachings of the Geeta. The goal is to make ancient wisdom easily accessible and relevant for today’s users through a simple, interactive chat interface.