Rate My Professor Ze Yang

Dr. Ding Ze Yang is a Lecturer in the Department of Electrical and Robotics Engineering at Monash University Malaysia, part of the Malaysia School of Engineering. He obtained his Bachelor of Engineering (Honours) in Electrical and Computer Systems Engineering from Monash University Malaysia, awarded on 16 November 2019, and his Doctor of Philosophy in Engineering from the same institution, awarded on 15 March 2023, with a thesis entitled 'Deep Learning for Reliable Soft Sensor Development'. His research focuses on industrial AI, particularly developing data-driven soft sensors for process monitoring in industrial systems. Key research interests include deep learning, data-driven modeling, process monitoring, soft sensors, machine intelligence, and autonomous systems. These efforts apply to predictive analytics, process optimization, intelligent control systems, and autonomous robotics in manufacturing, transportation, energy, and logistics. Specific projects encompass monitoring and control of industrial processes using deep learning, fault diagnosis and predictive maintenance, knowledge discovery for explainable AI in industrial systems, reinforcement learning for process optimization, and autonomous systems for transportation and logistics.

In his academic career, Dr. Yang serves as a lecturer, teaching courses such as Neural Networks and Deep Learning (ECE4179) and Probability Models in Engineering (ECE2191). He has authored publications in high-impact journals including IEEE Transactions on Industrial Informatics and Soft Robotics, along with numerous conference papers. Notable recent works include 'Multi-Objective Optimization of Cloud Energy Storage Placement and Sizing in Peer-to-Peer Energy Market under Line Congestion' (2025, with J. Q. Ling, W. S. Tan, Y. K. Wu), 'ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing' (2025, with H. H. Tew et al.), 'Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers' (2025, with J. K. Yap et al.), 'Cross-Domain Transfer Learning Using Attention Latent Features for Multi-Agent Trajectory Prediction' (2024, with J. Q. Loh et al.), and 'Deep Learning Based Hybrid Assisted Stochastic Unit Commitment with Transportable Energy Storage' (2024, with J.-S. Chia, W.-S. Tan, Y.-K. Wu), all presented at IEEE conferences. Dr. Yang is accepting PhD students in deep learning, industrial AI, soft sensors, machine intelligence, and autonomous systems. His research supports UN Sustainable Development Goals such as good health and well-being, affordable and clean energy, and sustainable cities and communities.