External Research Grants

Computer Generated Forces (CGFs) are artificial entities in tactical simulations modelled after human behaviour. In a previous project with DSO, Intelligent Computer Generated Forces (iCGF) with adaptive capabilities have been developed to provide a viable alternative to script-based CGF. Through reinforcement learning, these iCGFs are designed to acquire knowledge while interacting and operating in a dynamic environment. This project with DSO builds on the capabilities developed in the previous project to enhance the study of iCGF by considering the following two key research questions; 1. How to scale up the applicability of iCGF, in terms of the number of iCGF entities as well as their operability in more complex scenarios and; 2. How to interpret and demonstrate the knowledge learned by iCGF in the form of high-level description.

What determines the coverage of Covid-19 and related political contestation in traditional and new media? In collaboration with the University of Bamberg in Germany, this project sets out to identify hidden power structures between media organizations in contemporary hybrid media systems in Germany, UK, USA and South Korea. This will provide insights about the determinants of information quality and the spread of misinformation during a large social crisis in media coverage.

This project addresses the inefficiencies in the lead generation, prospecting, engagement and qualification processes which are important stages in the acquisition of high net worth and ultrahigh net worth clients in a financial services business. The current process of digital leads generation and prequalifying is inefficient as it involves the manual qualification of large volumes of names collected from social media posts and marketing campaigns. Furthermore, client advisors do not have enough actionable insights on the leads provided by marketing and prefer to follow their own warm leads (e.g. networking) instead of the ones assigned to them. The objective of this project is to significantly improve the efficiency and effectiveness of these activities by adopting modern digital technologies including Artificial Intelligence models and Big Data analytics.

This research project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No. AISG-100E-2020-062).

Current code comparison technology mostly only work in a single language, failing to support inter-language migration. This project looks at closing this gap, through exploring new techniques for comparing code similarity across different languages and paradigms. Through this research, the team aims to improve the quality of software and reduce software development cost.

This research project studies the AI/IP interface, in particular the potential for IP to promote and protect AI innovation from the perspectives of AI professionals, IP practitioners and policy-makers. With these, and comparative understandings, will follow the prospect of enhancing Singapore’s position as an international leader in the AI/IP interface.

This project addresses the inefficiencies in the KYC (Know Your Customer) and Due Diligence processes which are critical yet resource-intensive activities in a financial services business. These activities are particularly complex in the context of Wealth Management, as compared to retail banking, where the client profiles of high net worth and ultrahigh net worth individuals are typically associated with a wider multi-national network of other family and business relationships and company structures and entities which need to be considered holistically in order to build a full understanding of their profile. The objective of this project is to significantly improve the efficiency and effectiveness of these activities by adopting modern digital technologies including Artificial Intelligence models and Big Data analytics.

This research project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No. AISG-100E-2020-058).

The Singapore Green Finance Centre (SGFC) is Singapore’s first research institute dedicated to green finance research and talent development. The SGFC will draw on the respective strengths of Imperial and SMU in climate science, financial economics, and sustainable investing – equipping professionals with new skills and developing a strong pipeline of green finance talent. Its multi-disciplinary research and training will enable financial institutions, corporates, and policymakers to improve the management of environmental risks, develop financial solutions to promote environmental sustainability, and design policies for a sustainable future.

Research

The SGFC will pursue foundational and multi-disciplinary research to help develop strategies for policy makers and financial institutions to support Asia’s transition to a low carbon future. The research will be co-created with industry to ensure applicability and relevance, and will cover three key themes:

• transforming businesses by integrating climate-related data and environmental, social & governance (ESG) considerations into decision-making
• designing policies and new initiatives that can improve the efficiency of green finance markets; and
• catalysing the development of green finance solutions.

Talent development

To equip professionals with skills in climate finance and applied knowledge in Asian markets, the SGFC will offer an array of courses across various levels – undergraduate, post-graduate, continuing and professional education. This will develop a strong pipeline of green finance talent which financial institutions and service providers can tap as they expand teams and deepen green finance capabilities to serve the growing needs of Singapore and the region.

Under the Fujitsu-SMU Urban Computing and Engineering (UNiCEN) Corp Lab, SMU has undertaken the Digital Platform Experimentation (DigiPlex) project with Fujitsu. The project was carried out using the Digital Annealer (DA), a quantum inspired-technology inspired by Fujitsu. Through the DigiPlex project, certain challenges in solving constrained optimization problems using such technology, and promising methods on tuning of the underlying model parameters to improve run time performance, have been identified. This project aims at developing hyper parameter tuning methodology, machine learning techniques, operations research algorithms, and software tools to enhance quantum-inspired techniques for solving large scale real-world combinatorial optimization problems.

This project aims to develop Fast-Adapted Neural Networks (FANNs) and provide specific solutions to equipping the Advanced Manufacturing and Engineering (AME) systems with FANNs. It considers a wide range of AME application examples such as visual inspection of new product parts and automated identification of product defects. It will improve the yield rate and reduce manufacturing costs, when FANNs-based devices are widely installed in the design, layout, fabrication, assembly, and testing processes of production lines. This research is supported by A*STAR under its AME YIRG Grant (Project No. A20E6c0101).

This collaboration with Coleridge Initiative aims to develop entity linking models that automatically identify datasets used in research publications to perform context-rich search and recommendation on research repositories.