Biography

Anurag Deo is a researcher and engineer specializing in Natural Language Processing, Generative AI, and Large Language Models. His work centers on building efficient, explainable LLM systems for clinical NLP, structured information extraction, and document intelligence using Retrieval-Augmented Generation (RAG). He works extensively with parameter-efficient fine-tuning methods such as LoRA and other PEFT approaches to adapt foundation models for high-impact, domain-specific tasks. His research interests include domain adaptation, instruction tuning, multimodal reasoning, and trustworthy AI systems for real-world deployment. Anurag combines a strong publication record with hands-on engineering expertise, enabling him to translate cutting-edge research into scalable, production-ready AI solutions. His broader goal is to develop robust and interpretable generative systems that bridge the gap between academic innovation and practical applications.