Mr Bao Doan
I am a Postdoctoral Research Fellow at UNSW Sydney, specialising in developing reliable Retrieval-Augmented Generation (RAG) systems that integrate large language models (LLMs) with external knowledge bases.
My research focuses on optimising transformer architectures for improved semantic retrieval, implementing advanced prompt engineering techniques to reduce hallucination, and developing robust evaluation frameworks for RAG performance.
I work extensively with vector embeddings, neural information retrieval, and fine-tuning methodologies to enhance the factual accuracy and reliability of LLM outputs in knowledge-intensive applications, while addressing scalability challenges in real-world RAG deployments across diverse domains.
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- Teaching and Supervision
My research specialises in the robustness and trustworthiness of deep neural networks, from Convolutional Neural Networks (CNNs) to Large Language Models (LLMs) and transformer architectures.
My research focuses on adversarial machine learning, developing sophisticated attack and defense mechanisms against threats including adversarial examples, data poisoning, and model inversion attacks, while exploring both "ML for security" and "security for ML" paradigms.
I investigate privacy-preserving techniques such as differential privacy and federated learning, and recently expanded into building secure Retrieval-Augmented Generation (RAG) systems using open-source LLMs, addressing unique challenges like prompt injection attacks, retrieval poisoning, and hallucination mitigation in trustworthy AI deployments.
My Research Supervision
I am currently supervising undergraduate & graduate students for their Thesis projects