Yuting Bai is an MPhil researcher at Curtin University whose work focuses on applying deep learning and materials informatics to hydrogen storage prediction in metal-organic frameworks (MOFs).

Research focus
His research spans scientific data engineering, feature extraction, graph neural networks, model evaluation, interpretability, and high-throughput virtual screening. He develops machine learning pipelines for experimentally synthesised and hypothetical MOFs to identify promising hydrogen-storage materials and translate model predictions into interpretable design insights.

Professional background
Alongside his research, Yuting works as an AI Engineer, developing reliable LLM applications, document automation, and knowledge retrieval solutions. Independently, he explores agent systems, tool orchestration, and open-source AI software. His broader interests lie at the intersection of scientific AI and software engineering.