External Research Grants

This project aims to design a hierarchical cross-network multi-agent Reinforcement-Learning-based trading strategy generator and examines governance framework for crypto asset markets.

This proposal contributes to Thrust 3 of the National Quantum Computing Hub (NQCH) that is focused on translational R&D, such as the development of libraries, prebuild models, and templates to enable easier and faster programming and developments of software applications by early adopters in the industry, government agencies and Institutes of Higher Learning (IHLs). This project aims to develop hybrid quantum-classical algorithms and tools that will contribute to the libraries and pre-build models for supply chain use cases. Compared with classical techniques, we aim to enhance the performance of the Sample Average Approximation (SAA) and Simulation Optimization, that is verifiable in today’s NISQ quantum hardware, and apply these algorithms to supply chain risk management contexts. It is anticipated that these algorithms will achieve higher-quality and computationally attractive solutions over pure classical algorithms.

This is a project under the AI Singapore 100 Experiments (Research) Programme. BIPO has a unique advantage in payroll processing and saw an opportunity to build a tool anchoring on downstream pay outcomes as an enabler in strategic design of a rostering tool, that should not only feedback about staff costs, productivity, and preferences, but also feedback on skills-based job evaluation and design. BIPO’s client pool in labour-intensive industries such as logistics, retail (restaurants, shops), call centers, healthcare and hospitality have an acute need for a rostering tool that is based on roles, skills and pay. In this project, we combine constraint programming with adaptive large neighborhood search to generate rosters according to rostering requirement and maximizing the preferences of employees. We also cover the dynamic setting where reinforcement learning is applied to prescribe changes to the roster due to changes in the environment.

This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-100E-2022-098). 

This project is an interdisciplinary and multi-institute work package, led by SMU, making use of the Digital Urban Climate Twin (DUCT) results from the first Cooling Singapore 2.0 work package to examine the urban climate risks and impacts from environmental and physiological perspectives. The objectives include (a.) investigating where and who in Singapore will be affected by excessive heat from urbanisation and climate change, and (b.) examining if existing measures, such as vegetation cover, will have reduced effectiveness in minimising heat exposure under a warming climate. Results from this project will aid in assessment and future policy development towards urban warmth solutions in Singapore.

This project studies a way to efficiently bootstrap graph neural networks (GNN), a deep learning technique on graphs. A graph (also called network) contains different entities, which are further linked based on their interactions, to form complex networks. However, to achieve optimal performance, for each graph and analytics task, GNNs require a large amount of task-specific labels, which are example cases happened in the past. Such labels are often unavailable or expensive to collect in large scale. In contrast, label-free graphs (i.e., graphs without task-specific labels) are more readily available in various domains. To overcome this critical limitation, the project team turn to GNN pre-training, which can efficiently bootstrap GNNs using label-free graphs and only a small amount of task-specific labels, to capture intrinsic graph properties that can be generalized across tasks and graphs in a domain. Practical applications of this research include fraud detection and anti-money laundry on financial networks, container demand and shipping prediction on supply chain networks and talent match on job/skill graphs.

To better understand gender gaps in opportunities and outcomes in the workplace, the team plans to examine the prevalence, causes, and potential remedies of gender discrimination across three organizational contexts. Part 1 of the project will focus on causes of discrimination in the selection of candidates for jobs and promotions. Part 2 will focus on performance evaluations, specifically how decision-makers may react differently to female and male employees who make errors on the job. Part 3 will examine the underlying reasons for gender gaps in negotiation outcomes as well as strategies to help promote fairer outcomes in compensation discussions.

The mechanism design literature hinges upon several assumptions, including (1) strategic sophistication - the ability of the individuals to think in complex ways, and (2) detailed knowledge of the environment. While these assumptions are standard in mechanism design, they are nevertheless very strong. “Real-life'' economic agents are not as rational as typically modeled. When agents have limited strategic sophistication, economists lose confidence in the performance of mechanisms that force participants to engage in complicated mental tasks. Furthermore, in realistic settings, the designer typically does not have detailed knowledge of the environment. Thus, a mechanism that performs well under strong assumptions of the environment might perform poorly when these assumptions turn out not to be true. The projects we propose here aim to further the understanding of mechanism design when the above-mentioned assumptions are relaxed.

The project aims to identify the nature of China’s influence on the international law governing the high seas. This research will comprehensively examine China’s strategies to expand its impact on global ocean governance in an era of geopolitical and environmental change. The project is expected to generate new knowledge for better understanding of China’s approaches to international law of the sea.

Modern Singapore is internationally renowned as a ‘Garden City’. Firmly entrenched in the official narrative as a linchpin of its national and global identities, the imagery of a verdant city-state serves as a reflection of Singapore’s economic prosperity along with the success of its governance model. Though largely attributable to the state-led greening campaign initiated in 1967 by Lee Kuan Yew, public parks, formal gardens, and roadside trees do not constitute the entirety of Singapore’s rich gardening heritage as a ‘Garden City’. Indeed, according to a survey conducted by the National Parks Board, approximately one in two respondents cultivate plants at home. Found in a wide range of residential and public settings, edible vernacular gardens are tightly interwoven into the fabric of everyday life as stylistically informal small-scale green spaces, cultivated by individuals and communities.

Spanning approximately two hundred years of Singapore’s modern history, this study will draw upon a wide array of textual and non-textual historical and contemporary sources to document gardening in Singapore from the 19th century to the present day. It will identify the ways in which historical gardening practices in Singapore have been continued, reinforced, and transformed into the contemporary period through building a body of new research and knowledge. In doing so, our proposed study will reflect an increased focus on ICH as part of the Our SG Heritage Plan and catalyze the writing of a new environmental history of Singapore, one which places ordinary people and practices in the foreground.

This collaboration develops “Sustainability and Commercial Law in Asia” as a focus area of research for the SMU Centre for Commercial Law in Asia, seeking to investigate the relationship between sustainability and commercial law developments in Asia and to propose suggestions for review and reform where appropriate.