External Research Grants

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.

This research project aims to document and analyse the state of legal innovation in the Asia Pacific region. Building upon the State of Legal Innovation in the Asia-Pacific Report 2019, a report will be produced introducing readers to recent legal technology and innovation developments in over 10 selected Asia Pacific countries, as well as an overview of legal innovation in these jurisdictions. A set of law firm case studies detailing industry efforts in the area will also be produced. The report is meant to serve as a roadmap for lawyers, courts, technologists, and policymakers to key legal tech and innovation trends on this side of the world.

Principal Research Collaborator: Josh Lee (Asia-Pacific Legal Innovation and Technology Association)

Smart systems are increasingly dependent on machine learning frameworks for their feature implementation. These frameworks are built on top of many third-party libraries, which depend on many others. Simply trusting and reusing a framework poses a security risk as the framework and third-party libraries it depends on can contain exploitable vulnerabilities. To mitigate this risk, this project will create an advanced solution that identifies vulnerabilities in popular machine learning frameworks.

This research collaboration with Zhejiang University seeks to unlock the power of large software data stored in open software repositories for automating three common software development tasks: coding (code completion), commenting (code summarization), and identification of software defects (defect prediction).

This project aims to provide early evidence on the value of blockchain applications by examining their impact on the issuance of asset-backed securities. In particular, the team would like to investigate whether asset-backed securities issued using the blockchain technology have lower yield and lower issuance cost as compared to other asset-backed securities.

Innovation has often been regarded as a key driver of organizational and individual level success in the 21st century. For example, the Singapore government has on many occasions emphasized the importance of innovation for the nation to stay ahead of competition and remain adaptive toward external changes and disruptions. Major economies such as U.S. and China have also identified innovation as key strategic thrusts. Toward this end, organizations all over the world have been actively seeking ways to increase their employees’ creativity and innovation effectiveness. However, academics and business observers have also noted that Asian firms tend to lag western firms in innovation in part because Asian cultures that tend to impose more, norms, rules, and constraints on people’s behaviours.

In this research project, the team will take the construct of cultural tightness to the organizational level to examine how the tightness of an organization’s culture might influence its creativity and innovative capabilities in the Asian context. This inquiry is important because national and regional culture is typically not within the control of an individual or a small group of people, regardless of how motivated they are. It is extremely unlikely for an organization or a CEO to change the culture of a country or a province/state. Yet, CEO’s and senior executives have the power to directly influence and shape the culture of their organizations. By understanding the effect of cultural tightness at the organizational level, the team can help organizations better harness the creativity and innovation benefits of their organizational culture. Specifically, knowledge gained from their research can help companies develop the appropriate set of rules, policies, and norms to maximize creativity and innovation. By focusing on the Asian context, the team will also shed light on the variations among Asian firms in the way they organize and how that affects creativity and innovation.

Singlish is an English Creole language used in Singapore and it is evolving. New words have been introduced from time to time making it difficult to track the development of this language. With Singlish being used largely in informal conversations, it is extremely challenging for any linguistic expert to keep track of its changes. It is therefore necessary to combine machine learning and human expertise in a crowdsourcing approach to construct and maintain a Singlish dictionary at speed and scale, with reasonable quality. This project will use a combination of machine learning and human efforts in a crowdsourcing approach to learn Singlish words so as to build some sort of Singlish dictionary.