Hello and welcome! I’m Phong Do — a first-year PhD student at the University of Warwick, UK, supervised by Dr. Gabriele Pergola. My research focuses on Retrieval-Augmented Generation (RAG) and Knowledge Graphs to advance document retrieval and improve NLP tasks such as question answering and machine reading comprehension.

My journey into AI began at the NLP@UIT lab at University of Information Technology in Ho Chi Minh City, where I specialized in question answering and machine reading comprehension as an undergraduate researcher. This early work laid the foundation for my long-term interest in building systems that can read, understand, and reason over text. After graduation, I continued my involvement with the lab and expanded my experience at Zalo AI, where I spent over three years working on large language models and speech processing. At Zalo, I contributed to speech technologies for Kiki Assistant, a Vietnamese virtual voice assistant installed in over one million cars, helping bring conversational AI into everyday life.

Beyond traditional NLP, I am also pursuing research in GeoAI — the intersection of geography and artificial intelligence. My interest lies in how spatial knowledge and geographic context can be modeled to improve information retrieval and reasoning, particularly in combination with RAG and Knowledge Graphs. By integrating linguistic and spatial perspectives, I aim to develop AI systems that not only understand language but also situate that understanding within the broader context of knowledge, culture, and place.

Research Interests

I’m passionate about building intelligent retrieval systems that can reason, generalize across modalities and languages, and interact with structured knowledge. My research focuses on two major directions: enhancing Retrieval-Augmented Generation (RAG), and exploring the intersection between Artificial Intelligence and Geography.

• 🔍 Retrieval-Augmented Generation: I’m interested in advancing RAG systems to make retrieval more intelligent, interpretable, and adaptable to complex data types.

  • 📘 Knowledge Graph Reasoning: Leveraging structured knowledge graphs to support logical reasoning and evidence chaining during retrieval.

  • 🌐 Multilingual & Multimodal Embedding: Designing unified embedding spaces for cross-language and cross-modal semantic search - enabling systems to search across text, images, and more.

• 🗺️ AI and Geography: As a geography enthusiast, exploring the intersection between spatial intelligence and artificial intelligence.

  • 🧭 Spatial RAG & Geographic Question Answering: Developing systems that understand and retrieve geospatial information, enabling models to answer geography-based questions and reason over maps, locations, and spatial relationships.

I am happy to chat and discuss potential collaborations. Please feel free to reach out to me via Email (phongdntvn@gmail.com).

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