Projects

OrbitAI - Satellite Collision Avoidance Simulation

Python, C#, Three.js, PyTorch, Unity

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An AI-powered satellite traffic management system using hybrid neural networks to predict and prevent collisions in increasingly crowded low Earth orbit.

• Implements a Gated Recurrent Network (GRU) to predict real-time satellite position trajectories over 180-step sequences using synthetic LEO satellite data.
• Utilizes Graph Neural Networks (GNN) to coordinate satellites as dynamic nodes and predict high-risk interactions between spacecraft in orbital space.
• Features a Unity-based 3D orbital simulator that visualizes collision scenarios and avoidance maneuvers with live AI predictions and trajectory modeling.
• Deploys the machine learning model on AWS EC2 with Flask server and WebSocket communication for real-time inference between simulation and AI backend.
• Addresses the critical problem of space debris collision avoidance as satellite populations are projected to exceed 100,000 active satellites in LEO by 2030.

Certification Blockchain System

Golang, Rust, PostgreSQL, React

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A tamper-proof blockchain system built in Go for recording and verifying event attendance certifications with cryptographic integrity and web interface.

• Implements a complete blockchain with SHA-256 hashing, block validation, and cryptographic chain linking to ensure data immutability.
• Features a web-based submission system with HTML forms for attendees to register their event participation through a Go HTTP server.
• Includes persistent JSON storage with automatic blockchain state saving and loading between application restarts.
• Provides QR code generation functionality to create quick links for easy access to the certification submission forms.
• Designed with extensible architecture supporting planned features like PostgreSQL integration, Rust validation engine, and React frontend

MOODSIC - Emotion Detecting Music Player

Python, Django, Typescript, React, OpenCV

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detects a user's emotional state through facial expression analysis using a webcam and recommends a song that matches or elevates their mood by integrating with Spotify's API.

• Frontend developed using React for a responsive UI with live webcam integration.
• Backend built with Flask (Python) to handle routing, process emotion data, and interact with Spotify’s API.
• Emotion detection implemented using OpenCV and a deep learning-based facial emotion recognition model.
• Spotify Web API used for authenticating, accessing playlists, and retrieving track details based on mood and genre.
• Challenges included emotion detection accuracy, managing Spotify API limitations, ensuring real-time performance, and achieving cross-browser compatibility.

GitHub Issues CLI

Rust

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A command-line tool for listing, creating, and managing GitHub issues directly from the terminal.

• Fetches and displays a list of issues from a GitHub repository.
• Supports filtering issues by status, labels, or assignee.
• Allows creating new issues without visiting GitHub web interface.
• Provides summary statistics like open vs closed issues and issue count per label.
• Designed as a lightweight CLI tool to streamline issue management for developers.

ImgCap - Automatic Image Captioning

Python, PyTorch

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A PyTorch-based image captioning model using CNN encoder and LSTM decoder architecture trained on the Flickr8K dataset to generate natural language descriptions of images.

• Implements an EncoderCNN class using pre-trained Inception v3 to extract visual features from input images and map them to embedding space.
• Features a DecoderRNN class with LSTM layers that processes image features and generates sequential word predictions for caption generation.
• Combines encoder and decoder in a unified pipeline (crpipe) class that handles end-to-end training and inference for image-to-text translation.
• Includes training infrastructure with configurable hyperparameters, checkpoint saving/loading, and TensorBoard integration for monitoring training progress.
• Provides caption generation functionality with beam search capabilities and vocabulary mapping to convert token predictions back to readable text.