(This is additional funding to SMU for the existing research project.) Digital wellbeing has arisen in public, governmental and policy discourse as a key measure of a person’s wellbeing through a healthy use of technology. This project aims to identify and measure digital wellbeing for digital readiness, inclusion and safety.
Through this project, the team will design and conduct a research study that aims to meet the following objectives: (1) to understand the salary trends and progression of accountants and/or accountancy graduates by showcasing the salary of accountancy job roles and/or salary trend of accountancy graduates over the years, and (2) to illustrate accountancy as the ticket to diverse careers by showcasing the enriching jobs across various industries through sharing of stories by ISCA members.
A trusted digital identity is an essential component for securely and conveniently accessing services and authorizing transactions in cyberspace. With the rapid development of decentralized technologies and applications, such as distributed ledgers, Web3, and decentralized finance, there is an urging demand for decentralized digital identities (DID), also known as self-sovereign identities, which empower end users to create, own and govern their digital identities and assets in an autonomous, reliable, and privacy-preserving manner. The overarching goal of this project is to develop and implement a trusted, versatile, reliable and user centric DID framework covering a complete DID lifecycle. Specifically, the project aims to investigate novel techniques for enabling key components and features that are either missing or inadequately addressed in the existing DID proposals. This research is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Trust Tech Funding Initiative.
This project focuses on “human capital development” that involves, as one of its core pillars, the ability to use AI to improve learning. Through this project, the SMU Research team will work with the MIT team to build up key AI based models for supporting interactive learning by supporting natural multi-modal question answering associated with learning tasks. The SMU team will also develop initial prototypes that embed such interactive learning in online learning platforms.
This project focuses on “immersive workplaces” that involve extensive and interactive co-working by humans and AI-enhanced agents/robots. The research will investigate techniques to combine the latent cognitive state of a human worker, with explicit instructions issued by humans using a natural mix of visual, verbal and gestural cues, to build powerful new capabilities for human-robot co-working in immersive workplaces and industrial settings. The SMU Research team will develop optimized AI models, as well as prototype pervasive systems, that allow a variety of robotic agents to disambiguate, comprehend and respond to commands issued by human workers.
In this project, we aim to develop a generic framework to coordinate human and AI for tackling practical decision-making problems, e.g., supply chain delivery planning and healthcare scheduling, for improving the operational effectiveness and efficiency of activities in different scenarios. We will develop various cutting-edge machine learning methods to build reliable, generalizable, and explainable AI models to assist human decision making in various complex and large-scale contexts.
Climate change is an increasingly urgent and existential crisis that threatens all sectors of society in all countries of Southeast Asia (SEA). The Climate Transformation Programme (CTP) aims to develop, inspire and accelerate knowledge-based solutions and educate future leaders to establish the stable climate and environment necessary for resilient, just, and sustainable Southeast Asian societies.
CTP will generate knowledge and innovation across disciplines including climate and Earth science, ecology, materials science, artificial intelligence, humanities, social sciences and the arts, finance, health, and engineering. It will also translate state-of-the-art scientific results into real world solutions for Singapore, and it will transfer these solutions to SEA and beyond.
CTP will help to better understand exactly how the climate is changing; to identify and develop ways to contribute to global climate change mitigation efforts; to better assess the environmental, social, and economic risks from climate change; and to design effective solutions to control, reduce and adapt to these risks.
To achieve these multiple objectives, CTP will integrate a matrix of six strategic clusters of research and three cross-cutting themes, where SMU faculty will participate in and contribute to Cluster 1 (Climate processes and extremes) and Cluster 5 (Climate and finance markets).
Cluster 1 will address key knowledge gaps in the understanding and modelling of the physical processes and impacts of climate change in Singapore and SEA. It will focus on (1) regional climate processes such as monsoons that influence weather patterns; and (2) extreme weather events including extreme heat and rainfalls, and droughts, and their consequences such as flooding and wildfires.
Cluster 5 will measure and price climate risk in financial markets, design innovative risk financing solutions to climate change, and quantify economic impact of climate policies. It will concentrate on the business risks and new opportunities posed by the impacts of climate change, the narrowing of the protection gap and transition to a low-carbon economy. It will price the climate risk exposure of various financial assets, and design sustainable risk financing solutions. This includes the economic impacts and effectiveness of carbon credit markets and ESG green financing. It will rewire the business model for long-term resilience and the transition to net zero emissions. It will explore new interdisciplinary approaches to ensuring sustainable and resilient supply chains and communications networks.
In this project, we propose to use a computational social science approach to analyse career trajectories using very large resume datasets combining social science with AI methods (i.e., Large Language Model-based methods augmented by knowledge graphs). The objective is to perform fine-grained analysis of career trajectory data determining the factors that contribute to career mobility as well as factors that prevent career mobility. Our new computational social science approach can be reused for future follow-up studies to reveal other detailed career trends and patterns. Hopefully, our new proposed work will also detect early signals on types of career trajectories and skills among specific demographic groups, as well as emerging trends that threaten employment, career progression and wellbeing of the workforce.
Recent advancements in AI make it possible to process large amounts of medical imaging data and replicate clinicians’ decisions with competitive performance. However, the adoption of AI in clinics has been challenging due to several issues, such as clinicians’ inability to understand how AI operates to trust and adopt it in practice. In this project, we aim to develop and evaluate a human-AI collaborative system and practices for improving collaboration between clinicians and AI in the context of head and neck cancer screening. This system learns representations of clinical videos to identify urgent referral cases and generates AI explanations on interactive visualizations to improve clinicians’ understanding of AI and their practices. After implementing the proposed system, we will conduct user studies to evaluate the effectiveness of the system.
The main goal of this project is to develop new technologies to test how well the perception module of an autonomous driving system functions and understand how perception errors impact other parts of the system, like decision-making. The project team aims to create innovative solutions to evaluate the performance of the perception module in autonomous driving. Throughout the project, the team will utilize software testing technologies, machine learning technologies, formal methods, and evolutionary algorithms to explain and develop their methods. The resulting technologies will contribute to improving the safety and security of autonomous vehicles from their development phase to actual use on the road.
