External Research Grants

As AI becomes ubiquitous, people’s trust in AI is actually dwindling. The key barrier to adopting AI is no longer technical in nature, but more about gaining stakeholders’ trust. Federated Learning (FL), in which training happens where data are stored and only model parameters leave the data silos, can help AI thrive in the privacy-focused regulatory environment. FL involves self-interested data owners collaboratively training machine learning models. In this way, end-users can become co-creators of AI solutions. To enable open collaboration among FL co-creators and enhance adoption of the federated learning paradigm, this project aims to develop the Trustworthy Federated Ubiquitous Learning (TrustFUL) framework, which will enable communities of data owners to self-organize during FL model training based on three notions of trust: 1) trust through transparency, 2) trust through fairness and 3) trust through robustness, without exposing sensitive local data. As a technology showcase, we will translate TrustFUL into an FL-powered AI model crowdsourcing platform to support AI solution co-creation.

In safety-critical applications (e.g., where human lives are at stake), it is crucial for humans to be well-trained to handle expected and unexpected scenarios of varying complexity. Paramedics frequently respond to life-or-death situations, and they must be trained to handle expected and unexpected situations with respect to patient condition and response to treatment effectively. Manned vessels in maritime traffic operate in environments with many other vehicles, and humans must be trained to safely navigate varied situations. A wide range of critical activities in crime response, healthcare, defence, and construction also require training to improve safety. Through this project, the team intends to develop and assess Explainable and tRustworthy (ExpeRt) AI (or Agent) Training Programs (ATPs) with feedback interfaces to adaptively train human(s) for safety-critical applications with showcase projects on emergency response and maritime navigation. The ExpeRt ATPs will generate safe, unexpected scenarios that adapt to observed learner deficiencies and yet provide fair and comprehensive coverage of all cases/situations. This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-017). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.

Singapore Management University (SMU) introduced in 2015 a major pedagogical programme of experiential learning for undergraduates, called “SMU-X.” In four years, it has grown from a pilot initiative with a limited number of courses, offered by a small team of enthusiastic and dedicated faculty, to a large programme reaching more than 3,600 student enrolment per year. As SMU-X enters its fifth year, this TRF project aims to evaluate the extent to which the programme has achieved its objectives. The evaluation results will help SMU to (1) enhance its SMU-X initiative for undergraduate students, (2) extend the concept to graduate students, and (3) to share its knowledge and expertise in experiential learning with other institutions of higher learning in Singapore and the region. The research will also contribute to a better understanding of how we should prepare students for a professional environment. Finally, we hope that our research may make a methodological contribution, helping others to measure the impact of experiential and problem-based learning methods.

Drawing on the research of Social Identity Theory and Identity Integration literature, this longitudinal study seeks to investigate the relationship between female students’ Gender-Professional Identity Integration (G–PII) and their creativity in STEM fields. The findings will inform the strategies of structuring STEM programs including the arrangement of mentors and the design of class exercise to incubate female students’ performance.

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).