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.
The objective of the proposed project is to explore, in close collaboration with a local air transport hub, the development, validation and testing of an integrated set of models, algorithms, and tools that will support the Stand Assignment Process, considering impacts on the activities and behavior of passengers within the terminals. The project will also assess the likely impacts of a new AI-based system on the range of affected stakeholders, involve managers and staff in the design process, and train them in the use and management of this technology. Similar use cases with a ride-hailing service provider will also be explored.
This research project aims to leverage Virtual Reality (VR) and Artificial Intelligence (AI) to improve public speaking skills through immersive, real-world scenario simulations. The project seeks to develop a VR system with AI-driven avatars that respond dynamically to a presenter’s body language and speech, enhancing the learning experience by providing interactive and personalized feedback. It addresses the scalability and resource limitations of traditional public speaking training by offering a virtual environment where students can practice and refine their skills without the need for a physical audience. The research will explore PresentationPro's effectiveness in helping students achieve learning outcomes in university public speaking programs and equip them with key skills for the future workplace. By incorporating advanced AI, machine learning, and VR technologies, PresentationPro aims to provide a realistic and accessible virtual practice experience that reduces public speaking anxiety and improves performance. The project will be assessed through pilot studies focusing on learning outcomes, system usability, and the immediate applicability of training in real-world settings.
The objective of this project is to enhance students’ comprehension, retention, and overall learning outcomes in programming by leveraging AI-enabled PromptTutor. It aims to design an AI-enabled intervention that prompts students to reflect on their completed tasks, address doubts in their reflections, and provides additional learning resources in a personalised and timely manner.
(This is additional funding to SMU for the existing research project.) This project aims to study how better care options can be provided and developed for the local community. The first study will centre around the awareness and preferences of Singapore residents aged 50-76 regarding Assisted Living, with a set of survey questions to be designed and fielded through the Singapore Life Panel. Other focus areas will be developed over the course of this 2-year collaboration based on up-and-coming topics as they emerge.
This two-year research project aims to provide a framework for measuring, valuing and reporting environmental and social impacts in impact investing, leveraging the Impact-Weighted Accounts Framework (IWAF) that SGFC has been co-developing with other academic and industry partners. It will also demonstrate how the IWAF can be applied to Asian organisations through pilot studies with companies and financial institutions in Singapore and China, and build an impact finance database that aligns with other similar initiatives in North America and Europe for future academic research.
In the past decade, mobile devices and Internet of Things (IoT) have become prevalent in our daily lives, both in business and social settings. The applications of such devices have also been growing exponentially due to the development of technologies that support artificial intelligence (AI) and cloud computing. The advances of cloud and mobile computing in the past decade have fundamentally reshaped the computing infrastructure used by individual, business, and government users into a distributed, heterogenous and collaborative system. Modern applications are hence often built as a fusion of data, software, and services from a mixture of stakeholders. Growing with this evolution are the deep-rooted security concerns over a broad spectrum of issues such as leakage of private data, infringement of software copyrights, and corruption of computation results.
This research programme gathers a core team of experts from the Nanyang Technological University (NTU) and the Singapore Management University (SMU) to tackle these security concerns. The SMU team aims to holistically address security challenges in the mobile and cloud computing ecosystem pivoting on the new so-called "confidential computing" techniques featuring hardware-based Trusted Execution Environments (TEEs). The research outcomes are expected to build up the infrastructure and capabilities towards a zero-trust computing domain for industry and government users.
Singapore's latest high-resolution climate change study, V3, led by NEA/CCRS can simulate future impacts of climate change under different scenarios. Climate change effects are complex and often uncertain in when they are likely to occur and how impactful they will be. Furthermore, it is challenging to engage with climate projections as future climate change impacts seem far removed from our immediate experiences.
Through this project supported by the NRF and NEA under the CISR funding initiative, the research team will map climate projections impacting Singapore into a risk assessment framework that will identify potential policy solutions to reduce future climate impacts and risks. Immersive virtual reality narratives will be constructed to operationalise the risk assessment framework and communicate the future impacts of climate change, along with the solution space, to policymakers and other stakeholders. Taken together, this will make the impacts of climate change directly relevant and provide a platform to translate climate science into effective policy.