ReQuest

A cross-platform full stack mobile app that helps users scan items, find recycling centers for that item, and earn points for recycling

• Utilizes Google Cloud Vision and Places APIs to scan items and locate nearby recycling centers
• Leverages a Llama model to assess recyclability and assign points with Firebase storing user information and tracking progress
• Created a dynamic database of 47 crowdsourced recycling locations regularly updated through web integration

Technologies:

React Native

TypeScript

Expo

Tailwind

Llama Model

Firebase

Google Cloud

PanicPrep AI

AI-powered full stack flashcard generator with account management, real-time storage, and payment integration.

• Firebase handles online storage and Clerk manages user authentication, ensuring secure access to flashcards and allowing creation of multiple flashcard sets
• Stripe API processes payments for premium features and subscriptions (Online storage and increased flashcard set size limit)
• Mixtral model via the Groq API generates relevant study content based on user preferences and progress

Technologies:

Firebase

Stripe API

Mixtral Model

Mui

React

Node.js

Next.js

Clerk

SnipDeck

Chrome extension for managing text snippets and screenshots with speech synthesis, local storage, and a dynamic interface.

• Chrome Extensions API is used to integrate the extension seamlessly with the browser
• Speech Synthesis API reads out snippets to users, improving accessibility
• JavaScript handles background processes for context menu functionality, taking screenshots, and WebP image compressing

Technologies:

Chrome Extensions API

Speech Synthesis API

JavaScript

HTML

CSS

Service Workers

Cyclistic Case Study

Case study analyzing Divvy's bikeshare data using R for data manipulation, visualization, and analysis

• tidyverse aids in data cleaning and manipulation for easier analysis
• geosphere calculates geographical distances to analyze cycling patterns
• lubridate processes dates to uncover trends over time
• leaflet creates interactive maps to visualize cycling routes and hotspots

Technologies:

R

tidyverse

leaflet

Data Analysis

S&P 500 Sector Portfolio Optimization

Optimization algorithm built with Python using machine learning, Monte Carlo simulations, and the Sharpe Ratio to predict optimal sector weightings

• Random forest regression predicts optimal portfolio sector weights
• Monte Carlo simulations optimize the weightings for risk-adjusted returns
• Pandas handles data manipulation, preparing historical stock returns for analysis

Technologies:

Python

Scikit-learn

Pandas

Jupyter Notebooks

Machine Learning

Full Stack Netflix Clone

Netflix clone built with to explore frontend development, authentication, and database management

• Next.js powers the server-side rendering and routing of the app
• MongoDB stores user data and movie information for easy querying
• Authentication system enables secure user logins and movie preferences

Technologies:

MIPS Math Memory Match

Memory match game built with MIPS assembly language to explore low-level programming and memory management

• MIPS assembly language handles all game logic, including user input,
  game state, and memory management
• Game logic includes shuffling cards, checking for matches, and keeping track of the score

Technologies:

OrgPortal (Work in Progress)

Full Stack web application built to support UTD's 450+ student organizations

• Enables student orgs to manage members, track engagement, gain actionable insights, and act as a discovery platform for students to explore different orgs
• Currently refining core features and preparing for deployment by improving scalability and data integrity

Technologies: