A personalized agentic AI study assistant that leverages official course information from Canvas LMS and user-uploaded documents. Implemented a multi-agent RAG pipeline using a LangGraph-based AI engine to minimize hallucinations. Designed two Fetch.ai agents: one for OCR of handwritten notes and miscellaneous study material, and another for sending emails to professors via the Gmail API. Built with React.js, Fetch.ai, Iris DBMS, LangGraph, and Flask.
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A novel encoder-decoder transformer architecture to convert images of scientific math equations to LaTeX. Developed a DenseNet encoder with 2D positional encoding, paired with a custom GPT-2-based transformer decoder and an extended RoBERTa-based tokenizer. Integrated Weights & Biases to track test metrics including perplexity, BLEU, and F1 throughout training. Built with PyTorch and Transformers.
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A comprehensive study tool that extracts LaTeX notes from class recordings and enables students to query all their study material. Implemented Gemini Pro Vision for OCR and conversion to clean LaTeX code, returned as PDF. Employed RAG using LangChain to fetch relevant data from a Pinecone vector database. Built with React.js, Flask, Pinecone, and LangChain. Check out the YouTube video.
↗ view projectLed the development of PocketPeds through UMD's App Dev Club — an application to improve pediatric healthcare by helping caretakers calculate safe dosages for pediatric OTC medications. Developed a robust backend integrating MongoDB, PostgreSQL, JWT authentication, and the FDA NDC API. Created an intuitive frontend for scanning barcodes, scheduling medications, and viewing dosage history. Built with React Native, Tailwind, Flask, and PostgreSQL.
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