Rate My Professor Tiong Teck Chai Tiong

Tiong Teck Chai Tiong serves as a Lecturer in the Department of Electrical and Computer Engineering at Curtin University Malaysia's Sarawak Campus in Miri, Malaysia. He holds a Master of Science degree in Electrical Engineering from a university in the United States. Tiong possesses professional affiliations as a Senior Member of the Institute of Electrical and Electronics Engineers (SMIEEE) and a Member of the Institution of Engineering and Technology (MIET). He is also chartered as an Engineer (CEng, UK), recognized as a European Engineer (Eur Ing), and holds International Professional Engineer status (IntPE, UK). In his tenure at Curtin University Malaysia, he has undertaken roles including coordinator for final-year projects, accreditation and industrial relations coordinator for the department, and member of faculty committees and working groups.

His research specializes in wireless communications and signal processing. Additional interests include power system stability, renewable energy, and machine learning applications in engineering contexts. Tiong is qualified to supervise PhD and MPhil students in electrical and computer engineering. He has authored several key publications, including "Deep Reinforcement Learning with Robust Deep Deterministic Policy Gradient" (2020), "Transceiver design for SWIPT multicasting MIMO relay systems" (2019), "Adaptive Modulation and Coding for LTE Wireless Communication" (2015), "Robust Transceiver in MIMO Relay Systems with Direct Link" (2018), "Review on Determinant Parameters of Genetic Algorithm Optimization" (2022), "Process Proportional-Integral PI Control with Deep Reinforcement Learning" (2023), and "WCDMA Network Planning and Optimisation" (2008). These contributions appear in IEEE conferences, IOP Materials Science and Engineering, and other peer-reviewed sources, advancing knowledge in areas such as MIMO relay systems, simultaneous wireless information and power transfer (SWIPT), LTE optimization, genetic algorithms, and reinforcement learning for control systems.