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Ten PolyU research projects granted Research Impact Fund and Collaborative Research Fund

11 Jan 2022

Research and Innovation

Ten PolyU research projects have been awarded with a total amount of $47 million from the Research Grants Council (RGC) for three of its funding schemes, namely, Research Impact Fund (RIF) 2021/22, Collaborative Research Fund (CRF) 2021/22 and the second round one-off CRF COVID-19 and Novel Infectious Diseases (NIR) Research Exercise. The RIF supports projects that have potential to translate the research findings and create impact that can benefit society. The CRF supports Research Equipment and Research Projects for acquisition of major research facilities or equipment, and promoting group research across disciplines respectively.

 

Prof Christopher CHAO, Vice President (Research and Innovation), congratulated the research teams and said, “We are grateful to get these encouraging results to support our colleagues to further their research endeavours. PolyU’s core research facilities support a wide range of research activities and it is the  University’s strategy to strengthen these facilities to support more interdisciplinary research. This year we rank first in the CRF Research Equipment Grants in terms of the number of projects supported. It helps build up our central research facilities and support our researchers to explore more innovative solutions to various societal  challenges.”

 

RGC Research Impact Fund 2021/22 – two funded projects being awarded a total amount of $8.8 million
Dr WEI Minchen, Tommy, Associate Professor, Department of Building Environment and Energy Engineering
Project Title: Deeper Understanding of Color Matching Mechanism for Developing High-quality Lighting and Imaging Systems

Dr HSU Li-Ta, Associate Head and Associate Professor, Limin Endowed Young Scholar in Aerospace Navigation, Department of Aeronautical and Aviation Engineering
Project Title: Reliable Multiagent Collaborative Global Navigation Satellite System Positioning for Intelligent Transportation Systems

 

RGC Collaborative Research Fund 2021/22 – five funded projects (three Research Equipment Grant and two Research Project Grant) with  a total amount of $24.1 million
Research Equipment Grant
Prof CHEN Xiaojun, Director, University Research Facility in Big Data Analytics, and Chair Professor, Department of Applied Mathematics      
Proposal Title: High Performance Deep Learning Clusters for Big Data Analytics

Prof YANG Mo, Associate Head (Research) and Professor, Department of Biomedical Engineering
Proposal Title: An Upright Multiphoton Microscope for Intravital Imaging and Optogenetic Studies

Prof WONG Raymond Wai-yeung, Dean, Faculty of Applied Science & Textiles, Chair Professor of Chemical Technology, Department of Applied Biology and Chemical Technology
Proposal Title: Advanced Fourier-transform Electron Paramagnetic Resonance Spectrometer for Molecular and Nano Functional Materials Research  

 

Research Project Grant 
Dr ZHAO Xin, Department of Biomedical Engineering
Project Title: Development of High-Resolution 3D Scaffolds with Biomimetic Triply Periodic Minimal Surface Structure for Bone Tissue Repair

Prof WONG Man Sing Charles, Professor, Department of Land Surveying and Geo-Informatics
Project title: Study of Carbon Sequestration in Hong Kong’s Vegetation: from Present to Future Prediction under Climate Change

 
Second round one-off CRF COVID-19 and Novel Infectious Diseases (NIR) Research Exercise – three funded projects being awarded with a total amount of $14.1 million
Ir Prof GUO Hai, Professor, Department of Civil and Environmental Engineering
Proposal Title: Is the Usual Social Distance Sufficient to Avoid Airborne Infection of Expiratory Droplets in Indoor Environments?

Dr SHIH Yi Teng, Assistant Professor, School of Design
Proposal Title: The Effect of Distance Design Collaboration Necessitated by COVID-19 on Brain Synchronicity in Teams Compared to Co-Located Design Collaboration  

Prof SHI Wenzhong John, Professor, Department of Land Surveying and Geo-Informatics
Proposal Title: Spatiotemporal Prediction and Real-time Early Warning of COVID-19 Onset Risk

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