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PolyU and CAS’s Joint Lab Develop Optimisation Tools for Medical Image Analytics, Digital Economics, and Smart Cities Awarded CAS-Croucher Funding Scheme

16 Sep 2022

Director of CAS AMSS-PolyU Joint Laboratory: Prof Chen Xiaojun (left2), Prof Yuan Yaxiang (right2); Associate Director of CAS AMSS-PolyU Joint Laboratory: Prof Qiao Zhonghua (left1), Prof Liu Xin (right1).

The CAS AMSS-PolyU Laboratory has three research groups with members from AMSS and PolyU and will capitalise on their specialities to extend the frontiers of research in Applied mathematics.


Jointly established  by the Hong Kong Polytechnic University and the Academy of Mathematics and Systems Science (AMSS) of the Chinese Academy of Sciences (CAS), the CAS AMSS-PolyU Joint Laboratory of Applied Mathematics was recently awarded the “CAS-Croucher Funding Scheme for Joint Laboratories 2022” with a total grant of HKD 3 million. PolyU and AMSS will each provide a matching fund of HKD 375,000 for the project, equivalent to HKD 750,000. 

Led by the two Directors , Prof Chen Xiaojun, Chair Professor of Applied Mathematics of PolyU and Prof Yuan Yaxiang, Academician of CAS, AMSS, this awarded project, entitled “Nonlinear Optimisation Theory, Algorithms and Applications”, aims to conduct a comprehensive mathematical investigation of nonlinear optimisation theory and build solid mathematical foundations for essential applications in data science, engineering and economics. 

Complementing the strengths of PolyU and the CAS research institutes, this project will develop advanced and cutting-edge deep learning software using new optimisation theory and algorithms to overcome the difficulties of the existing software in terms of speed, accuracy, robustness and complexity. Efficient optimisation models and tools using artificial neural network-based machine learning algorithms will also be developed for medical image analytics, digital economy and smart cities.

Although prediction processing is in general widely investigated in economics and smart cities, to tackle the lack of problem-specific network designs for rapid responses to market risk and disasters, such as emergent incidents and extreme weather, this project will develop fast optimisation algorithms for prediction problems in the digital economy and smart cities, making full use of different features of data and images. 

CAS-Croucher Funding Scheme for Joint Laboratories 
Launched by the Chinese Academy of Sciences (CAS) and the Croucher Foundation Limited, this scheme aims to encourage researchers in Hong Kong universities and CAS research institutes to work together on highly specific scientific topics. The selection  has been conducted every two years since 2004 and evaluated by national and international experts.


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