Daniel Su is a PhD candidate at The University of Western Australia
Daniel’s research focuses on developing an integrated, multi-task deep learning framework for advanced geological analysis.
Prior to commencing his PhD, Daniel completed an Honours degree at The University of Western Australia, where his research investigated the stability of reinforcement learning for query augmentation using large language models. He also holds a Master of Information Technology and a Bachelor of Science from the University of Melbourne. Through both academic research and industry collaboration, including a research partnership with BHP, Daniel has developed expertise in machine learning, information retrieval, retrieval-augmented generation (RAG) systems and large language models for real-world applications.
Daniel’s research will strengthen the Centre’s capability in geospatial artificial intelligence and multimodal data integration, supporting the development of next-generation digital tools for mineral exploration. His work will contribute to smarter, data-driven exploration strategies and help accelerate Australia’s leadership in critical minerals research and geodata intelligence.
