Xiao Cai is currently a visiting PhD student at the IfM, under the supervision of Professor Ajith Parlikad. His research primarily focuses on health monitoring and remaining useful life (RUL) prediction for complex systems. He has developed a novel prediction method based on the Wiener process for two-phase degradation systems, which incorporates real-world physical damage observations, such as cutting tool wear thickness and blade crack length, into the modeling process.

In addition to probabilistic modeling, Xiao is also exploring graph neural network (GNN)-based prediction methods, which integrate prior domain knowledge to enhance model performance. At the IfM, his current research involves alarm prediction for Radio Access Networks (RAN) using graph-based approaches.

His work has been published in leading journals including IEEE Transactions on Industrial Electronics (TIE), IEEE Transactions on Industrial Informatics (TII), Mechanical Systems and Signal Processing (MSSP), and Reliability Engineering & System Safety (RESS).

Xiao received his B.S. and M.S. degrees in Mechanical Engineering from Xi’an Jiaotong University, China, in 2019 and 2022, respectively. He is currently pursuing a Ph.D. in Systems Engineering at the City University of Hong Kong.