projects
Practical builds focused on measurable impact.
EdgeStyleGAN is a lightweight variant of StyleGAN optimized for efficient deployment on resource-constrained devices such as mobile phones and edge computing devices. Our approach achieves significant reductions in model size and inference time while preserving high-quality image generation capabilities.
This project fine-tunes Qwen3-4B-Instruct on the Indic subset of the Aya dataset using Unsloth. The goal is to improve instruction-following and conversational abilities of the model in Indian languages such as Hindi, Bengali, Tamil, Telugu, and others.
Engineered a hybrid recommendation engine integrating Truncated SVD–based collaborative filtering, content-based scoring via cosine similarity, and demographic risk profiling on the FAR-Trans dataset (introduced at IJCAI-2024).
Built a multi-modal character attribute extraction pipeline using CLIP (image) and BART (text), capable of processing 9 structured attributes (age, gender, ethnicity, hairstyle, hair color, eye color, body type, clothing style).
A comprehensive web application that uses RAG (Retrieval-Augmented Generation) to help students upload PDFs, organize by subject/topic, and instantly generate summaries, quizzes, and intelligent Q&A content for fast, effective last-minute studying.