๐ Graduate Student MCS @ Rice University
๐ป Passionate about Full-Stack Development, AI, and System Design
๐ Currently based in Houston, TX / Open to global opportunities
Transform research papers into production-ready code with AI
An advanced multi-agent system powered by Google Gemini that automatically analyzes academic papers using RAG (Retrieval-Augmented Generation) and generates complete ML implementations. Features dual-mode research capabilities, intelligent task routing, and supports PyTorch/TensorFlow/JAX frameworks.
Key Features:
- ๐ RAG-Enhanced Paper Analysis: PDF parsing, ChromaDB indexing, semantic search
- ๐ค Dual-Mode Research Agent: Paper analysis + general research capabilities
- ๐ป Auto Code Generation: Complete ML projects with training scripts and documentation
- ๐ฏ Smart Orchestration: LLM-based task classification and multi-agent coordination
Tech Stack: Python Google Gemini 2.5 ChromaDB PyMuPDF arXiv API FastAPI Docker
Example Usage:
You: "Reproduce Attention Is All You Need paper"
# โ Downloads PDF from arXiv
# โ Analyzes using RAG (ChromaDB + semantic search)
# โ Generates complete PyTorch implementation
# โ Creates training scripts, configs, and documentationLive Demo on Hugging Face Spaces
A Knowledge-Enhanced RAG system that lets you chat with your PDF documents. Upload any PDF and ask questionsโthe assistant uses document knowledge when relevant, and general knowledge when not.
Tech Stack: Google Gemini 2.5 Pro ChromaDB Gradio Python
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Tech Stack: React 19 TypeScript Tailwind CSS Gemini API WeChat Mini Program
๐ RiceBook
A full-stack social media application built with the MERN stack. Features user authentication (including Google OAuth), post creation, social interactions, and dark mode support.
Live Demo: Frontend | Backend API
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Thanks for visiting my GitHub! ๐