External Research Grants

This project builds upon algorithms previously developed by the Prof Lau Hoong Chuin under the Quantum Engineering Programme 2.0 initiative - specifically in addressing variants of the News Vendor and Knapsack Problems - to tackle the increasing complexity of consumer demand and fluctuating market dynamics in logistics. In collaboration with ST Logistics (STL), the project will develop a hybrid quantum-classical model capable of jointly performing demand forecasting and inventory optimization. The goal is to deliver a proof-of-concept (POC) solution with computational efficiency for complex, real-world logistics scenarios provided by STL.

The overall objective of this project is to develop systematic and rigorous ways of safeguarding foundation models, including large models such as large language models (LLMs) as well as large multi-modal models (LMMs), against state-of-the-art and future security attacks. While there have been many bandage-like mitigation approaches on mitigating security attacks on LMs, they are far from having a lasting effect. The reason is that these mitigation approaches are treating the symptoms rather than fixing the causes of the problems. The team aims to develop techniques and systems which can detect and defeat a variety of security attacks on large models, through either prompting, finetuning or instruction- tuning, for the goal of jailbreaking, or embedding backdoors. This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Trust Tech Funding Initiative.

This project will establish a Nature-Food Futures Learning Precinct at SMU. Located in the city centre, the Precinct will be a hub for hands-on learning and research about native edible plants at the intersection of the Green Plan's Food and Nature goals.

Instructors today face increasing challenges in designing and delivering courses that effectively balance cognitive load, align with intended learning outcomes, and actively engage diverse learners. Traditional lecture slides and assessments often lack structure, personalization, and interactivity leading to passive learning, reduced motivation, and inconsistent achievement of educational goals. Furthermore, evaluating and improving teaching delivery remains largely subjective, with limited tools to analyze real-time classroom engagement or instructional clarity. This project offers AI-driven analysis and nudging mechanisms that align content with learning objectives through semantic and topical modeling, while embedding cognitive design strategies to manage learners’ mental load effectively. This research addresses these challenges by exploring how AI and analytics can enhance course content design, assessment, and delivery in a data-informed and scalable manner.

Funded by the Ministry of Law and housed at the Centre for Digital Law, this three-year project will build a next-generation, open empirical legal database for Singapore. As the first comprehensive open-source database of structured legal data on the country's cases, statutes, courts, and legal scholarship, the database will enable rigorous computational and empirical studies of Singapore law by local and international scholars, support the development and evaluation of legaltech and AI systems, and position Singapore's legal sector as a leader in technology innovation. The database will be developed with inputs from key legal stakeholders such as the government, law firms, in-house legal teams, and legaltech startups, and make use of modern technologies including large language models to facilitate data extraction. By making high-quality legal data freely accessible through modern APIs and documentation, the project aims to strengthen Singapore’s existing legal data infrastructure for the digital age and demonstrates Singapore’s commitment to developing the legaltech ecosystem.

This project aims to develop the first high-resolution regional weather reanalysis dataset for Singapore and Southeast Asia, covering the period from 1990s to 2025. By integrating sparse historical ground and satellite observations with next-generation numerical weather prediction (NWP) models and advanced data assimilation techniques, the project will produce a comprehensive and accurate historical weather record tailored for tropical climates.

Key features of the project include:

• Generation of a high-resolution (at least 4km) reanalysis dataset using cutting-edge NWP systems and ensemble-based data assimilation methods.
• Compilation and quality control of a regional historical observation datastore, encompassing both international and local data sources.
• Comprehensive scientific analysis of both observation and reanalysis datasets to validate accuracy, identify trends, and provide insights into regional climate dynamics.
• Development of a Regional Reanalysis Product Catalogue to support planning & operational decision-making in aviation, maritime, energy, and urban planning sectors.

This project will position Singapore as a regional leader in climate and weather science, enabling AI-powered forecasting and evidenced – based policymaking through robust historical climate data. It will also contribute to international collaboration and technological advancements in regional reanalysis.

This proposed study investigates the role of digital career profiling tools (i.e., MySkillsFuture Singapore) in shaping career decision-making, upskilling behaviour, and workforce outcomes in Singapore. Focusing on the MySkillsFuture portal and similar profiling platforms, the study aims to address critical gaps in understanding how these tools influence career development trajectories across different demographic groups and occupational roles.

Eye tracking has emerged as a powerful, non-invasive window into neurological and ocular health, offering early biomarkers for conditions such as Parkinson’s disease, Alzheimer’s disease, and glaucoma. However, current RGB camera–based systems are bulky, power-intensive, and limited in their ability to capture the subtle, high-frequency micro-movements of the pupil that are critical for early diagnosis. To overcome these limitations, this project introduces SynapSee, a novel end-to-end wearable system that integrates event cameras with a multi-light active probing setup and computationally optimised algorithms for real-time, fine-grained pupil tracking. Unlike conventional eye trackers, event cameras operate at sub-microsecond latencies and asynchronously capture changes in light intensity, making them uniquely suited for high-velocity saccades and micro-movements. By exploiting “dark” and “bright” pupil effects through multi-light probing, SynapSee reduces extraneous event volume, enabling low-power and efficient processing. The system is further enhanced by hybrid spiking neural networks, adaptive sensing algorithms, and collaborative offloading to nearby devices, achieving both accuracy and energy efficiency. We will validate SynapSee in two exemplar clinical contexts: (i) detecting early neurodegenerative changes in Parkinson’s disease and (ii) identifying the onset of low-vision conditions such as macular degeneration, cataracts, and glaucoma. Longitudinal user and patient studies, conducted in collaboration with clinical partners, will establish discriminative ocular biomarkers and benchmark the system’s sensitivity and specificity. By enabling unobtrusive, continuous, and large-scale monitoring via smart glasses, SynapSee has the potential to transform preventive healthcare, offering clinicians powerful tools for early intervention and personalised disease management.

This project will explore how two rapidly evolving and important topics—artificial intelligence (AI) and urban climate governance—are interacting in cities across South and Southeast Asia. The project will explore the complexities of autonomizing urban governance at the intersection of AI and climate change and will focus on the challenges that emerge when integrating AI into environmental governance in cities. The empirical focus will be on Singapore, Bangkok, Jakarta, and Bangalore.

This project will investigate how a global competition for desired skills shapes individuals’ migration trajectories. We focus on the case of migrant nurses, a profession projected to face a severe labour shortage in the coming years. This project furthers the MOE’s mission of producing new knowledge by examining the factors that shape people’s multinational migration trajectories, and it will be the first systematic study on how competing actors can influence where nurses migrate, the time they spend in each place, and how they respond to recruitment strategies or policy change. Such findings will contribute to both academic scholarship and policy discussions on the unequal distribution of healthcare labour. This issue has critical implications for aging societies like Singapore, where nurse shortages remain a pressing challenge for local health institutions.