External Research Grants

(This is additional funding to SMU with a project extension.)

This research project aims to study the working preferences of older Singaporeans and understand their perceptions towards upskilling, reskilling and the types of employment that may be meaningful for them. The results from this research are expected to shed insights and inform the design of a jobs-skills-learning recommender, the SkillsFuture Career Transition Programme (SCTP) and other services and facilities catered to older adults’ needs.

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 are being explored.

This project targets human capital development through AI-driven learning, with a focus on both childhood and adult learners. SMU researchers will develop AI-based tutoring technologies that enhance engagement and support during self-paced learning sessions. The project includes collaboration with organizations such as Yayasan Mendaki and SMU Academy. Key objectives are to capture multi-modal learner queries – visual, verbal, and gestural – using advanced sensors, and to build AI models for interactive question answering and generation in response to such queries. Focusing initially on mathematics problems, these models will also adapt the learning content (while formally assuring the correctness of auto-generated new content) based on assessments of learners’ current levels of competency and capability. The goal is to create new AI-powered online platforms to improve learning outcomes and personalize educational experiences across diverse learner populations.

This project focuses on enabling immersive AI-assisted human-robot collaboration in dynamic industrial environments such as aviation and marine maintenance. Assistive agents deployed in robots or other wearable devices must comprehend and respond to human-issued instructions involving spatial and temporal references, adapting their behaviour in real-time. SMU researchers aim to develop lightweight, energy-efficient AI models and pervasive systems that support comprehension of such multi-modal instructions – using visual, verbal, and gestural cues– and relate them to the 3D environment captured using sensors like RGB video, LIDAR, and neuromorphic cameras. Objectives include optimizing the execution of grounding tasks (associating instructions with specific real-world objects) for moving objects using video data and developing light-weight techniques for enhanced robotic spatial reasoning and planning (e.g., navigation to retrieve specific objects). These innovations will allow robotic agents to better interpret human commands and improve task execution, ultimately enhancing safety, productivity, and the adaptability of joint human-robot collaborative work in real-world settings.

(This is additional funding to SMU with a project extension.)

This project, led by A/Prof Iris Rawtaer (Sengkang General Hospital) aims to utilise multimodal sensor networks for early detection of cognitive decline. Under this project, the SKH team will oversee the project operations, screening recruitment, psychometric evaluation, data analysis, data interpretation, reporting and answer of clinical research hypotheses. The SMU team will collaborate with SKH to provide technical expertise for this study by ensuring safe implementation and maintenance of the sensors in the homes of the participants, provide the sensor obtained data to the clinical team and apply artificial intelligence methods for predictive modelling.

This research collaboration aims to examine the interrelated dimensions of governance, national identity, and belonging among Singaporeans citizens aged 60 and above. The research ultimately aims to contribute to a deeper understanding of how government performance and political narratives influence citizen engagement and well-being in Singapore, thereby providing valuable recommendations for enhancing social cohesion and democratic participation.

(This is additional funding to SMU for Stage 1b of the project.)

State-of-the-art visual perception models in autonomous vehicles (AV) fail in the physical world when meeting adversarially designed physical objects/environmental conditions. The main reason is that they are trained with discretely-sampled samples and can hardly cover all possibilities in the real world. Although effective, existing physical attacks consider one or two physical factors and cannot simulate dynamic entities (e.g., moving cars or persons, street structures) and environment factors (e.g., weather variation and light variation) jointly. Meanwhile, most defence methods like denoising or adversarial training (AT) mainly rely on single-view or single-modal information, neglecting the multi-view cameras and different modality sensors on the AV, which contain rich complementary information. The above challenges in both attacks and defenses are caused by the lack of a continuous and unified scene representation for the AV scenarios. Motivated by the above limitations, this project firstly aims to develop a unified AV scene representation based on the neural implicit representation to generate realistic new scenes. With this representation, we will develop extensive physical attacks, multi-view & multi-modal defenses, as well as a more complete evaluation framework. Specifically, the project will build a unified physical attack framework against AV perception models, which can adversarially optimize the physical-related parameters and generate more threatening examples that could happen in the real world. Furthermore, the project will build the multi-view and multi-modal defensive methods including a data reconstruction framework to reconstruct clean inputs and a novel ‘adversarial training’ method, i.e., adversarial repairing that enhances the robustness of the deep models with guidance of collected adversarial scenes. Finally, a robust-oriented explainable method will be developed to understand the behaviors of visual perception models under physical adversarial attacks and robustness enhancement.

Retail banks use real-time monitoring, machine learning, security checks, and rule-based systems to detect fraud by spotting deviations from normal customer behaviour. Quantum computing could dramatically boost these systems by processing vast datasets faster, enhancing pattern recognition, anomaly detection, and encryption. This project will build on prior quantum innovations and partnerships to identify and validate quantum algorithms that outperform existing fraud detection methods in accuracy and speed. It will also estimate required quantum resources, protect intellectual property, and train staff in quantum techniques, aiming for commercial advantage and contributing valuable knowledge to the financial industry’s fight against fraud.

Singapore’s openness - as a hub for the global exchange of goods, ideas, and people – brings many opportunities but also leaves its local firms and workers uniquely susceptible to shocks originating outside the country. With increasing geopolitical and economic uncertainty, understanding the mechanisms through which global shocks (such as trade wars and supply chain disruptions) propagate across firms' linkages becomes particularly relevant for the economy. The proposed research program seeks to establish datasets on firm-to-firm supply linkages for the Singapore economy, and to identify the mechanisms via which external and internal shocks transmit via these linkages to affect the firm-level performances, such as productivity, labor demand, and innovation activities. Once established, the local economy’s firm-to-firm dataset will open up a new landscape for academic research as well as policy analysis, given Singapore’s uniquely high degrees of participation in the global value chain, heavy reliance on multinational enterprises, and strong presence of service sectors.

This project aims to equip Android application analysers with AI-driven input fuzzer and output interpreter to make them usable products even for those without domain expertise.