Lingnan President honoured with prestigious IEEE award

On his first overseas trip as the new President of Lingnan University, Professor Joe Qin found himself in the spotlight. He attended the 7th IEEE Conference on Control Technology and Applications (CCTA) in Barbados from August 15-19, where he was invited to receive a prestigious award and deliver a plenary speech on the latest developments in the rapidly evolving field of machine learning and data analytics.


The IEEE (Institute of Electrical and Electronics Engineers) is the world’s largest technical professional organisation with a stated mission of advancing technology for the benefit of society at large. 


Its CSS (Control Systems Society) Transition to Practice Award has been presented annually since 2009. This award recognizes outstanding collaborative scientific interactions between industry, research laboratories, and the academic community.


The primary objective is to acknowledge instances where breakthroughs in basic controls and system theory have been successfully applied in practical situations. Typically, this results in greater speed and efficiency in manufacturing or production processes, often with significant commercial impact.


In selecting Professor Qin as the first recipient of the award from a university in Hong Kong and Greater China, the committee acknowledged his seminal research work conducted in the United States, and its practical implications for the semiconductor industry.


The citation highlighted his distinguished contributions to the field of data-driven control engineering. Specifically, it recognized the methodological advances and knowledge transfer he led in model predictive control, systems safety, health monitoring, and diagnosis.


In his plenary speech after accepting the award, Professor Qin reflected on his academic career and introduced a new framework for dynamic latent variable (DLV) analytics and application for"process troubleshooting". More broadly, he emphasized the greater need for domain knowledge in machine learning and data analytics.


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