The Faculty of Science is eager to transform ideas and research findings into applications in life through supporting innovative undertakings by faculty members, researchers, and students, hoping to promote knowledge transfer and benefit the society in the long run. The effort of our Faculty in promoting knowledge transfer is exemplified by the impact showcases below.
Big Data and Statistical Learning for Portfolio Risk Management (Department of Statistics)
Prof. WONG Hoi Ying (Department of Statistics)
The massive growth of the financial market creates challenges to the portfolio risk measurement and investment decisions. Professor WONG and Professor SIT’s research on statistical learning and big data methods has delivered considerable economic impacts on financial technology. Several key statistical learning methodologies were implemented in the development of algorithmic trading and risk management platform, which help the integration of estimation and optimization procedures together. The platform has been adopted by an international asset management firm to construct portfolio selection strategies and calculate risk in the mutual fund; it has also helped enhanced investment performance (e.g. projected annualised return of a fund increased by 12.6%) and given effective stop-loss signals to the corporation.
WONG and SIT would like to make their risk calculation framework a public good after gaining practical experience from the fund. A simplified version of the risk calculator for some selected popular derivatives was launched as an online open-access educational platform developed by the Department of Statistics, CUHK for practitioners and public to understand and appreciate statistical learning theory for portfolio risk management. This platform, revealing their research in portfolio risk measurement, has aroused public awareness about the investment of financial derivatives.
Colloidal Plasmonic Metal Nanocrystals: A New Page in Food Safety and Various Photonic Applications (Department of Physics)
Prof. WANG Jianfang (Department of Physics)
Professor WANG’s research group has developed robust methods for the synthesis of different noble metal nanocrystals with exquisitely controlled geometric shapes and sizes at purities >90%, with responsive wavelengths widely variable from the visible (~400 nm) to mid-infrared (~10 μm) region. This patented technology has been exploited by three spin-out companies, reaching over 1,000 customers in more than 30 countries and regions. The new technology has also had a broad impact on economy, R&D, and industries ranging from medicine, diagnostics, biotechnology to optoelectronics, etc. The team has also invented smart tags using the metal nanocrystals, as well as the application on spectral detection instruments in different fields for monitoring the quality of food and beverages, and the safety of drugs and explosives.
His research work was showcased to the public both locally and internationally. WANG was invited to exhibit his research work on metal nanocrystals in the InnoCarnival 2018 organised by the Innovation and Technology Commission of HKSAR, and join the 47th International Exhibition of Inventions Geneva in 2019, in which he won the Bronze Medal.
Three representative colloidal plasmonic noble metal nanocrystal products in shapes of bipyramid, sphere and rod, synthesized with WANG’s novel methods.
Novel Fabrication of Hollow Particles Deployed in White Inks and Sunscreen Products (Department of Chemistry)
Prof. NGAI To (Department of Chemistry)
Professor NGAI’s team has developed novel methods for the synthesis of submicron hollow based on particle-stabilized emulsions, making a major impact in the ink and cosmetic industries through their applications. The white ink fabricated using the hollow particle produces less sedimentation and gives higher white opacity. A Hong Kong-based technology company applies this brand new technique to white ink formulation in digital printing and has received a revenue of several millions HKD in 2018 by selling the products to many renowned electronic companies including Lenovo, ZTE, etc.
This hollow particle technology also attracted the investment in sunscreen and daily cosmetic product development from leading chemical company, BASF. NGAI’s novel technology in future sunscreen products could resolve the issue of microplastics and thus eradicate the harmful effects found in existing sunscreen products to environment, safeguarding the aquatic ecosystems in the long run.
Novel Mathematical Methods Significantly Improves Efficiency and Accuracy for Computer Graphics and Medical Imaging Industries (Department of Mathematics)
Prof. LUI Lok Ming Ronald (Department of Mathematics)
The dilemma between accuracy and efficiency for conventional 3D imaging acquisition has constrained the development of computer graphics and medical imaging industries. Professor LUI’s team has pioneered research in the development of Computational Quasiconformal Geometry (CQC) and advanced algorithms for surface parameterization, “Teichmuller parameterization”, with 30% enhancement in efficiency and real-time high-precision texture mapping of 3D objects when compared with the conventional methodologies. The technology was exploited by a US-based company to launch the new 3D scanner series with market-leading inimitable high-precision and develop an algorithm that is adopted by Blizzard, Siemens, and Intel. The research has also been adopted by a medical technology company as a tool useful in diagnosing early Alzheimer’s disease. Based on the shape analysis of hippocampal surfaces, this tool allows physicians to intervene the neurodegeneration before symptoms occur.
Example of texture maps on surface meshes
Left: Two raw 3D meshes representing human faces. Right: 2D images are mapped onto the surface meshes through quasiconformal parameterizations for surface textures. Accurate texture mapping is crucial in computer graphics for realistic visualization of 3D models.
CQC can also be applied to various medical applications, such as the shape analysis of hippocampal surfaces, assisting physicians to diagnose the Alzheimer’s disease.
Stress Tolerant Soybeans Enable Climate-smart and Sustainable Agriculture on Marginal Lands in China (School of Life Sciences)
Prof. LAM Hon Ming (School of Life Sciences)
Gansu Province located in the northwestern China is a place where precipitation is scanty and unpredictable year-round, placing limitations on the development of local agriculture. Professor LAM and his team have decoded wild and cultivated soybean genome and developed three new salinity and drought tolerant soybeans, namely Longhuang 1, Longhuang 2 and Longhuang 3, in collaborations with local farmers in Gansu. According to the estimation of the local seed station, the accumulated acreages of Longhuang 1, 2 and 3 in total have exceeded 24,200 hectares (2016-2020), bringing an estimated financial benefit of RMB 31.6 million (2016-2020). Smallholder farmers in Gansu have earned extra income to improve their livelihoods by growing these novel soybean varieties.
Furthermore, these soybeans were successfully used in intercropping (e.g. with maize, flax, wheat, fruit trees, etc.) and restoration of abandoned lands in arid regions, as well as soil replenishment in remote villages of high altitude. The biological nitrogen-fixing feature of soybean also helps reduce the use of fertilizers and hence CO2 emissions, lowering the PM 2.5 emissions.
With the promising research deliverables, “STEAM@soybean”, a spin-out education programme, has recently been awarded the Quality Education Fund in 2019 to promote knowledge integrating science and technology, and value education to high school students.
(Right) Professor LAM Hon Ming and (Left) his collaborator, ZHANG Guo Hong, Researcher at Dryland Agriculture Institute, Gansu Academy of Agricultural Sciences
Large-scale experimental fields of Longhuang soybean cultivars in Gansu
Uncovering the Cryosphere with Artificial Intelligence (Earth System Science Programme)
Prof. LIU Lin (Earth System Science Programme)
Monitoring the cryosphere comprising glaciers, ice caps, sea ice, permafrost, and many more in a sustained manner requires constant effort. Remote sensing imagery taken by satellites is one of the helpful tools to monitor the physical properties of a designated area. The long-used AI framework such as “DeepLab”, was initially developed for interpreting daily images with functions like differentiating cats and dogs from pictures, and significant errors might occur when examining satellite images. Recently, Professor LIU Lin’s team has launched “DeepThaw”, a revolutionary deep-learning method which is capable of analysing and identifying more than 800 thermokarst landforms from satellite images. After countless times of supervised learning on thermokarst features, mapping and modelling, “DeepThaw” is now a useful and effective tool which can ease the burden of scientists on analysis work and minimise human errors. Scientists can use the time saved to conduct more frequent monitoring on the cryospheric changes to better understand the underlying mechanisms, and to provide early intervention and have potential threat forecasting.
LIU’s team applied the newly developed AI-based tool to analyse the satellite images of Jakobshavn Isbrae, Greenland. The glacier has significantly retreated in just five years; “Calving Front” (red line), the junction of glaciers and the sea, is important for the stability of the entire ice sheet.
