Computer Engineering Student & AI/ML Enthusiast
I am passionate about building intelligent systems that solve real-world problems. Focused on RAG systems, machine learning, and applications where accuracy and reliability matter the most.
View My WorkI am a final-year Computer Engineering student with a passion for artificial intelligence and machine learning. Through hands-on projects and continuous learning, I've developed expertise in RAG systems, prompt engineering, and ML pipelines. I believe in building solutions that prioritize accuracy over complexity, especially in critical domains like healthcare, legal, and governance.
My academic journey has been complemented by practical projects where I've applied cutting-edge technologies to solve real-world challenges. I'm particularly interested in the intersection of AI and human-centered problem solving.
A modular RAG system built with Gemini 2.0 Flash, LangChain, and FAISS. Designed for critical domains like law, medicine, and governance where accuracy is paramount. Emphasizes truthful, non-hallucinatory responses through curated knowledge bases.
THYNK is a reimagined, adaptive successor to PsyRAG. It lets users upload knowledge, tune retrieval parameters, and generate grounded answers through an interactive UI — all while ensuring factual, non-hallucinatory responses.
Designed for researchers and professionals who need flexible, reliable knowledge retrieval.
Stanford Online
Credential ID: VXI1NOO8OIFJ
I'm always interested in discussing AI/ML projects, research opportunities, and innovative solutions to real-world problems. Whether you're looking for technical collaboration or want to explore new ideas, I'd love to hear from you.