A personalized meal plan recommender system that leverages machine learning to provide tailored nutrition recommendations while providing insightful visualizations.
- Developing interactive meal exploration and categorization
- Desiging visual comparison of nutritional information
- Creating health and demographic-aware filtering system
React.js
Python
A RAG-based chatbot for the University of South Carolina, designed to assist students, faculty, and staff with information and resources.
- Developing a conversational AI system using the RAG framework, enabling natural language interactions with the chatbot
- Designing a user-friendly interface for the chatbot, allowing users to ask questions, get answers, and explore campus services
Python
LLMs + RAG
A novel approach combining semantic segmentation with neural style transfer for precise artistic control.
- Developed a novel approach for segment-based neural style transfer, combining AdaIN layers for real-time style transfer with the Segment Anything model for accurate segmentation
- Implemented an interactive user interface, enabling user-guided selection of content, style, target regions, and style loss weights for creative exploration
- The project offered a user-centric solution for artistic image manipulation, surpassing the limitations of traditional methods by providing localized style control
Python
PyTorch
Computer Vision
Neural Networks
Style Transfer
An advanced image colorization system using convolutional autoencoders to transform black and white photographs into vibrant color images.
- Developed and trained a convolutional autoencoder for accurate image colorization of black and white photographs using Python and PyTorch
- Trained the model on nearly 28,000 images real-world images from the Google Landmarks dataset
Python
PyTorch
Deep Learning
Computer Vision
Autoencoders