External Research Grants

Island Science will look at the interlinked history of Singapore and other islands in the Pacific and Indian Oceans including, but not limited to, Ceylon, Christmas Island, Papua New Guinea, the Solomon Islands and Fiji, through the lens of sciences that related to the rubber industry and agricultural science during the British colonial era prior to the Fall of Singapore in 1942. These sciences (including the interlinked fields of soil, planting, breeding and tapping technologies) will operate as the lens to explore Singapore’s place in a regional and global network of knowledge, in a tangential shift from conventional histories of the local rubber industry which have tended to view the industry’s development from an economic or developmental standpoint. The project’s chronological is aimed at covering the early days of the rubber’s growth in Ceylon and Malaya and the main period for British-led rubber expansion and experimental schemes in the Pacific islands.

Specifically, the project looks to Singapore as a hub in the flow of ideas from and to the Malayan plantations, experimental sites and research institutes of the region. For example, how and what knowledge circulated through colonial agricultural departments, botanical gardens, scientific institutions, scientific journals, public service handbooks, and planters’ associations, perhaps through the situation and movement of scientific officers, the writing, publication and dissemination of research papers or technical guidance, the establishment and funding of regional research institutions or, as the location of central management for overseas plantation ventures.

Singapore’s Ministry of Education has introduced multiple reforms over the past two decades in a bid to reduce the education system’s emphasis on academic grades and open up pathways for non-academic aptitudes to be recognised and rewarded. One of the stated goals of this policy approach is to broaden opportunities for social mobility. In place of a society organised along a narrow hierarchy of academic merit, the vision is of a “broader meritocracy” where students are able to find success in the particular domain that matches their particular interests and skill sets. However, the realisation of this vision depends critically on how stakeholders, such as families, receive and respond to the reforms. Ultimately, it is the interaction between policies and families that shapes the terms of the educational competition that students find themselves in. Through in-depth interviews with parents of different educational backgrounds, this project seeks to understand what feedback parents receive from schools about their children, how they make sense of it, and how they respond to it. This work will help us better understand the shape of the educational competition in this changed landscape, as well as the processes shaping inequality and social mobility within it.

As Singapore continues to grow economically, academics, and social sector professionals have pointed out that the problems of those left behind seem to be increasingly complex. Noting this paradox, Minister Ong Ye Kung talked about how when more families are uplifted, challenges faced by those who remain poor become more difficult. The proposed study seeks to understand the state of intergenerational mobility among public rental housing residents in Singapore, the factors that influence this, and how rental housing residents see the socioeconomic progress in Singapore in recent years. Specifically, the PI will examine the social, economic, and psychological forces that come together to shape decisions that potentially hinder or bolster intergenerational mobility. In doing so, this investigation aims to increase our empirical understanding of the issue while creating a more comprehensive theoretical structure to identify and understand the kinds of choices lower income families make with intergenerational impacts. One that integrates macro structural factors with micro-level considerations. Ultimately, the goal is for the findings to inform initiatives that attempt to lift this most vulnerable group in Singapore out of the poverty trap and break the intergenerational transmission of vulnerability.

(Note: This is an additional funding coming to SMU from funding agency.)

This research project is commissioned by the Ministry of Sustainability and the Environment (MSE) to conduct an annual survey aimed at measuring and tracking public satisfaction and perceptions towards public cleanliness and public hygiene in Singapore. Under the collaboration, SMU will work with MSE to review the survey questionnaire and analyse the data collected to arrive at recommendations on improving existing policies and efforts towards public cleanliness.

This project aims to help lawyers stay in the game by connecting individual purpose with the lawyer’s work and surfacing common mindsets, with a view to reframing perspectives on work and supporting more sustainable legal careers.

AI for Program Reasoning is a research program that uses generative AI to advance the science of verifying that software behaves as intended. This programme is Co-Led by Prof Cristian CADAR from Imperial Global Singapore (IGS) and Prof Abhik Roychoudhury from National University of Singapore (NUS). It pursues three connected goals: (1) automatically generating program analysis tools for under-served domains such as scientific languages and floating-point-heavy code; (2) training large language models to genuinely reason about program verification rather than just predict next tokens; and (3) building AI agents that automate formal verification by inferring specifications and jointly repairing faulty code and failed proofs. SMU partners IGS and NUS in this research program and focuses on the second goal.

This research/project is supported by the National Research Foundation, Singapore under its Artificial Intelligence (AI)-for-Science (AI4S) Challenge (Award ID: NRF-AI4SCH-2025-0003).

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.

The HOPE (Human-Centered Operational Pandemic Resilience) programme aims to develop the next generation of AI-enabled decision systems that strengthen national preparedness and response to pandemics and other large-scale crises. Integrating Artificial Intelligence, Operations Research, and Cyber-Physical-Social (CPS) systems, HOPE seeks to transform crisis management from reactive response to proactive resilience.

The programme is organized around three pillars: Resilient Supply Chains and Logistics, Resilient Emergency Response, and Resilient Healthcare Operations, supported by a unifying CPS platform that integrates data, predictive analytics, optimization, simulation, and human-centered decision support. Through digital twins, learning-enabled planning, and multi-agent coordination technologies, HOPE will enable policymakers and operational agencies to evaluate interventions, allocate resources efficiently, and respond adaptively under uncertainty.

Working closely with government agencies, healthcare providers, and industry stakeholders, HOPE will translate research into deployable solutions that enhance societal resilience, protect lives and livelihoods, and establish Singapore as a global leader in AI-driven crisis preparedness and operational resilience.

Singapore’s transition into a super-aged society, characterised by extended life expectancy, ultra-low fertility, and emerging singlehood, poses urgent challenges to sustaining economic growth, health outcomes, and manpower supply. There is a widening ten-year gap between life expectancy (LE) and health-adjusted life expectancy (HALE), indicating that longer lives are not necessarily lived in good health. This research programme seeks to promote healthspan, which should improve HALE and eventually narrow the gap between HALE and LE. The key research question guiding the programme is: How do features of the built, lived and social environments shape pro-health behaviours and well-being among older adults, and together contribute to extending healthspan?

This research aims to improve how delivery routes are planned in complex, real-world cities like Singapore, where traffic rules, road layouts, and constraints make routing much harder than simple map problems. The team is developing a new AI system that goes beyond current tools by (i) handling realistic and uneven travel conditions; (ii) understanding practical constraints (like delivery rules) more intelligently; and (iii) allowing users to simply describe their needs in plain language instead of using technical inputs.

The significance of this research is that it could make delivery and transport systems faster and cheaper, while also making advanced routing technology accessible to non-experts – helping smaller businesses and improving overall urban efficiency.

This project studies how large language models can help experts understand software when only the compiled program, or binary code, is available. Binary analysis is important for finding hidden security weaknesses, studying malware, and examining old or third-party software when the original source code cannot be accessed. It is also very difficult, especially when software has been deliberately disguised to resist inspection.

The project will first evaluate how well modern AI models perform on key binary analysis tasks. It will then develop two new AI powered tools. The first will help recover readable assembly code from heavily disguised software. The second will compare two binaries by generating pseudocode and identifying meaningful differences. Together, these tools aim to make software analysis faster, more accurate, and more effective.

The findings could help cybersecurity professionals detect malware and software vulnerabilities more efficiently, strengthen trust in software supply chains, and improve digital security more broadly. The project will also support education and future innovation through open source tools, datasets, and training opportunities.