I am interested in Software Development, Data Science, and Machine Learning. I enjoy building web applications, solving complex problems, and working on innovative projects. I have experience with technologies like React, Node.js, and AWS, and I am passionate about continuous learning and exploring new technologies.
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Building scalable and efficient web applications, managing front-end and back-end development, and integrating APIs to deliver seamless user experiences.
Analyzing large datasets, creating predictive models, and leveraging machine learning algorithms to gain valuable insights and automate decision-making.
Building and utilizing AI-powered tools, integrating large language models into applications, and exploring techniques like RAG and embeddings to create intelligent, grounded user experiences.
Python
JavaScript
Java
C++
React.js
Node.js
HTML
CSS
SQL
MongoDB
Linux
API Development
Spring Boot
A full-stack RAG-based study tool enabling users to upload lecture notes (PDF/TXT) and query them in natural language, with answers grounded strictly in the user's own course material. Features a document ingestion pipeline that chunks uploaded text and generates embeddings via OpenAI's text-embedding-3-small model, using cosine similarity search to retrieve the most relevant chunks and feed them into GPT-4o-mini, reducing hallucinated answers. Built with a Spring Boot (Kotlin) REST backend and React/TypeScript frontend supporting course organization, document upload, and persistent multi-session chat history.
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Secured 1st place in the EWB UofT Hackathon by presenting MealSimple’s innovative approach to addressing food insecurity for Students. Leveraged Google Maps API to seamlessly connect users with nearby food banks. Integrated TheMealDB API to provide over 300 personalized meal recommendations based on available ingredients. Utilized GPT-4 to dynamically generate recipe suggestions tailored to user preferences, budget constraints, and time availability.
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This is a full-stack Expense Tracker application built using the MERN stack (MongoDB, Express.js, React, and Node.js). The app allows users to track their income, expenses, and investments. Users can also visualize their financial data through charts powered by Chart.js.
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A Train Management System built with Python, HTML, and CSS that monitors, tracks, and controls traffic in an effecienct manner. The systems can create train schedules, show times/routes, and coordinate trains. Built in a team of 5 in an agile enviornment.
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A react face recognition app. This app detects faces from images using the Clarifai REST API. Used a PostgreSQL database to store user login and registration information. Created a server using Express to handle requests between the Front-End and the Database
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