I'm a CS undergraduate driven by an unwavering passion for artificial intelligence. I'm particularly fascinated by its potential to solve complex problems and make groundbreaking discoveries when applied to science. I possess working knowledge in Computer Vision, Natural Language Processing, and Reinforcement Learning. My ongoing projects and self-study keep my skills sharp. An avid reader with a curious mind, I'm committed to becoming a skilled AI practitioner who can contribute to the advancement of both the field and our understanding of the reality.
This project showcases a cutting-edge financial fraud detection system built using Graph Neural Networks (GNNs). By leveraging graph-structured data and advanced techniques like spectral subgraph sampling and task-specific weight sharing, the system achieves a significant boost in precision and overall performance. The solution is containerized and deployed as a robust API for practical usability.
An AI-powered legal research agent designed to revolutionize the way legal professionals access and analyze information. By leveraging advanced Retrieval-Augmented Generation (RAG) techniques, this system offers a highly efficient and intelligent solution for navigating complex legal documents. Implemented a custom retriever by fine-tuning on domain specific dataset and query routing using Llama Index
Fine-tuned DeBERTa for real-time sentiment analysis of financial news and historical trends of past month to fecilitate investors and other stakeholders. With a Spring Boot backend offering secure APIs, caching, and data persistence. Deployed for production use, providing quick insights for financial decision-makers.
