External Research Grants

This project, led by A/Prof Iris Rawtaer (SKH) aims to utilise multimodal sensor networks for early detection of cognitive decline. Under this project, the SKH and NUS 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 and NUS 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 project is set to advance the security landscape of emerging technologies in Web 3, including pattern and model-based fraud detection and knowledge graph-based reasoning, in order to address the various issues and chaos in the Web3 domain and establish a comprehensive set of compliance standards.

This project is a joint study between Deloitte and Touche, Institute of Singapore Chartered Accountants, Singapore Management University and Singapore Manufacturing Federation. The study aims to shed light on (1) the current state of play for integrating sustainability into corporate strategies and business models in Singapore’s manufacturing sector, (2) the sustainability opportunities and risks in the manufacturing sector, and (3) the skills needed to fulfil the emerging role of accountancy and finance professionals as champions of sustainability in the manufacturing sector, especially for small-and-medium enterprises. The research findings are expected to raise awareness of sustainability opportunities and risks for manufacturing companies and to encourage more accountancy and finance professionals to support companies in their sustainability transformation.

This is a project under the AI Singapore 100 Experiments Programme. The project focuses on the healthcare industry resource management where there is a complex relationship not just among the various manpower types (doctors, nurses) but also with the patient lifecycle leadtimes, geo-location, medical equipment and facility needed to perform surgeries and patient care. Manpower shortage has birthed conservative and static long-term planning solutions without considering these upstream data flows. In post-covid world today, this project could bring more potential solutions to the manpower allocation and development problem, especially when demand changes acutely. The project sponsor, BIPO Service (Singapore) Pte Ltd believes that an AI-driven, short-input-to-output cycle HR system streaming in “demand”-pulled patient lifecycle data can allocate and inform skills development not only for full time, but part time workforce.

This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-100E-2023-118).

Most conversational systems today are not very good at adapting to new or unexpected situations when serving the end user in a dynamic environment. Models trained on fixed training datasets often fail easily in practical application scenarios. Existing methods for the fundamental task of conversation understanding rely heavily on training slot-filling models with a predefined ontology. For example, given an utterance such as “book a table for two persons in Blu Kouzina,” the models classify it into one of the predetermined intents book-table, predict specific values such as “two persons” and “Blu Kouzina” to fill predefined slots number_of_people and restaurant_name, respectively. The agent’s inherent conversation ontology comprises these intents, slots, and corresponding values. When end users say things outside of the predefined ontology, the agent tends to misunderstand the utterance and may cause critical errors. The aim of this project is to investigate how conversational agents can proactively detect new intents, values, and slots, and expand their conversation ontology on-the-fly to handle unseen situations better during deployment.

This project will create new knowledge derived from historical sources to benefit the academic and scientific communities of Singapore in understanding long-term regional rainfall variability. This benefits Singapore by revealing long-term trends and extremes, critical to water security and climate-change preparedness now, and in the future. This benefits society by helping scholars and government in managing water-related risk. Principal Investigator: Holly Yang

The objective of this proposed research is to examine the relationship between managers’ incentives to meet or beat earnings expectations and employee mental well-being. Using data collected from a mental health mobile app, the team will explore whether and how pressure to meet firms’ financial reporting objectives affect the mental health of lower level employees and their tendency to engage in misreporting.

In this proposed study, the team aims to examine two research questions related to Environment, Social and Governance (ESG) reporting divergence. First, the team will investigate the negative consequences of ESG reporting divergence in the absence of mandatory ESG reporting requirements. Second, they will examine the benefits of mandatory ESG reporting requirements for capital markets. In answering these questions, they aim to provide important policy implications on whether standardised ESG reporting improves the comparability of ESG reporting across firms globally and enhances the usefulness of ESG information for capital market participants.

This project aims to understand how Singaporeans respond to the current state of socioeconomic diversity (SED) and whether it shapes class relations. This will provide important insights into how future changes in SED may affect Singapore’s social compact. Critically, understanding how SED affects class relations will inform the targets of social intervention for mitigating Singapore’s emerging class divide.

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.