External Research Grants

Principal Investigator: Swapna Gottipati
Affiliation of PI: School of Computing and Information Systems
Funding Source: SkillsFuture Singapore
Project Synopsis: 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.

Principal Investigator: Thivya Kandappu
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education
Project Synopsis: 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.

Principal Investigator: Zhu Bin
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education
Project Synopsis: This project focuses on creating self-adaptive embodied agents capable of perceiving and planning in dynamic real-world environments, addressing current challenges like hallucinated plans, poor object tracking and inflexible execution. It employs retrieval-augmented planning, fine-grained environment understanding, and adaptive plan refinement using large multimodal models, validated through simulations and real robots in household tasks. Expected outcomes include new methods for adaptive planning and perception, a kitchen activity video dataset, and demonstrations in domestic scenarios, with broad applications in autonomous vehicles and assistive devices. The initiative aims to impact daily living and healthcare, especially eldercare in Singapore, aligning with national priorities to enhance AI leadership and support the Smart Nation agenda.

Principal Investigator: He Shengfeng
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education
Project Synopsis: This proposal presents OMNICON, a comprehensive framework for generating realistic long-term multi-human motions with environmental context. By designing novel motion representations with generative solutions, OMNICON addresses critical challenges in long-term motion generation, multi-human interactions, and motion-with-context synthesis. Designed to advance applications across animation, gaming, virtual reality, and robotics, OMNICON leverages principles from physics and spatial reasoning to produce temporally consistent, contextually adaptive, and socially coherent motion sequences.

Principal Investigator: David Lo
Affiliation of PI: School of Computing and Information Systems
Funding Source: Home Team Science and Technology Agency
Project Synopsis: This project, conducted in collaboration with HTX, explores the use of Generative AI (GAI) to advance scientific computing and strengthen cloud security and cybersecurity resilience. This project looks to address deep research challenges in building intelligent, domain-specific automation. This is done by leveraging LLMs for computational chemistry, cloud configuration security and developing robust defence strategies to protect AI systems for use in mission-critical settings.

Principal Investigator: He Shengfeng
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore
Project Synopsis: The increasing realism and accessibility of AI-generated and AI-edited videos threaten public trust, information integrity, and digital security. From misinformation campaigns to identity fraud, such manipulated content can cause real-world harm. Current detection systems are limited: they often focus narrowly on facial deepfakes, lack cultural and linguistic diversity, offer little interpretability, and struggle to adapt to new manipulation techniques. Additionally, most systems emphasize passive detection, without offering mechanisms for content traceability or origin verification. This bilateral research project between Singapore Management University (SMU) and Sungkyunkwan University (SKKU) aims to address these challenges by developing an interpretable, adaptive, and globally deployable deepfake detection and protection system, tailored to the languages, dialects, and socio-cultural contexts of Singapore and South Korea. This research/project is supported by the National Research Foundation Singapore under the AI Singapore Programme (AISG Award No: AISG4-TC-2025-018-SGKR).
* This research/project is supported by the National Research Foundation Singapore under the AI Singapore Programme (AISG Award No: AISG4-TC-2025-018-SGKR).

Principal Investigator: Dai Bing Tian
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore
Project Synopsis: Singapore and New Zealand both use interRAI, a standardised assessment tool that supports the care of older adults. While interRAI is reliable and effective, integrating Artificial Intelligence (AI) presents a transformative opportunity to enhance healthy ageing and support older people to live longer, more independent lives. Our project brings together clinicians and researchers from the University of Otago, Singapore Management University, University of Canterbury, and University of Auckland. We will identify how to effectively integrate AI into the interRAI assessment, risk prediction, and care planning process to improve efficiency, consistency, and personalisation of care. We will achieve this with a three-pronged approach: 1. AI-assisted Assessments: By partially automating the currently manual interRAI process, we can reduce assessment time by 50% while improving accuracy. We will integrate structured health data and multimedia inputs to generate enriched assessments. 2. AI-enhanced Risk Prediction: We will develop predictive models for outcomes such as fracture risk, cognitive decline, and depression. These models will be embedded into interRAI software to support timely, targeted interventions. 3. AI-driven Personalised Care Plans: We will create dynamic, user-friendly care plans using a knowledge-based AI system enhanced by large language models. These plans will be tailored for patients, families, and clinicians, ensuring clarity and actionable guidance. With support from New Zealand’s Health NZ and ACC, and Singapore’s Agency for Integrated Care, Kwong Wai Shiu Hospital, NWC Longevity Practice, and 59 Socio-Techno Ventures, this initiative will augment existing systems to deliver scalable, cost-effective improvements to aged-care while growing our respective AI sectors. This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Technology Challenge – Leveraging AI for Healthy Ageing (AISG Award No: AISG4-TC-2025-015-SGNZ). 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.
* This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Technology Challenge – Leveraging AI for Healthy Ageing (AISG Award No: AISG4-TC-2025-015-SGNZ). 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.

Co-Principal Investigator: Tan Ah Hwee
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: Ministry of Health
Project Synopsis: Mild Cognitive Impairment (MCI), a precursor to dementia, offers a chance for intervention to delay dementia onset and facilitate advanced planning. In our ongoing project Sensors In-home for Elder Wellbeing (SINEW) with Sengkang General Hospital (SKH), we have the first longitudinal cohort in Asia from which clinically meaningful behavioural and digital biomarkers are captured with consumer-grade low-cost sensors installed in the homes of older adults. We have also successfully demonstrated proof-of-concept using sensors and machine learning to obtain digital phenotypes for accurate classification of MCI in a home-based setting. In this SINEW+ project, we shall build on the SINEW cohort to validate and refine our explainable predictive modelling approach for early detection of MCI. By leveraging advanced AI models and multi-modal data, we aim to implement and evaluate these solutions in real-world community settings, focusing on their cost-effectiveness and scalability to ensure broad adoption.

Principal Investigator: Wang Hai
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore-MIT Alliance for Research and Technology Centre
Project Synopsis: 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.

Principal Investigator: Archan Misra
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore-MIT Alliance for Research and Technology Centre
Project Synopsis: 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.

Principal Investigator: Archan Misra
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore-MIT Alliance for Research and Technology Centre
Project Synopsis: 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.

Principal Investigator: Tan Ah Hwee
Affiliation of PI: School of Computing and Information Systems
Funding Source: Sengkang General Hospital
Project Synopsis: (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.

Principal Investigator: Xie Xiaofei
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore’s Robust AI Grand Challenge
Project Synopsis: (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.

Principal Investigator: Paul Robert Griffin
Affiliation of PI: School of Computing and Information Systems
Funding Source: Oversea-Chinese Banking Corporation
Project Synopsis: 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.

Principal Investigator: Xie Xiaofei
Affiliation of PI: School of Computing and Information Systems
Funding Source: CyberSG R&D Programme Office
Project Synopsis: Large Language Models (LLMs) are increasingly applied to sectors such as healthcare, finance, software development and autonomous driving. However, their complex and interconnected supply chains—including data pipelines, inference frameworks, software dependencies, and deployment infrastructures—introduce significant security, reliability, and ethical risks. These complexities amplify vulnerabilities and increase the potential for system-wide failures, necessitating a holistic, system-level approach to risk identification and mitigation. This project aims to systematically address these risks through three key objectives: (1) developing comprehensive risk assessment methodologies for the entire LLM supply chain, (2) designing LLM-specific cyber insurance products to mitigate potential losses, and (3) collaborating with industry partners to ensure practical adoption and real-world impact. By tackling these challenges, the project will enhance the trustworthiness, security, and sustainability of LLM deployment across critical domains.

Co-Principal Investigator: Liao Lizi
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: AI Singapore's National Multi-modal LLM Programme Research Grant Call
Project Synopsis: This project tackles critical challenges in the development and deployment of Multimodal Large Foundation Models (MLFMs), which are capable of understanding and generating content across text, image, video, and audio modalities. While current MLFMs exhibit impressive performance, their trustworthiness, accuracy, and high operational costs limit their accessibility—especially for smaller research groups and organizations. To address these gaps, the project focuses on two key innovations: (1) developing fine-grained "super alignment" techniques to reduce hallucinations and ensure model outputs align with human values, and (2) creating a scalable, low-cost multi-agent framework composed of smaller specialized models (8-15 billion parameters) that work collaboratively on complex tasks. These innovations will be powered by Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with AI Feedback (RLAIF), enabling continuous refinement and adaptation. The research will be validated through real-world applications such as video generation and multimodal chatbots, demonstrating both practical utility and cross-domain adaptability. Ultimately, this work aims to democratize access to advanced AI, supporting Singapore’s strategic goal of building inclusive, trustworthy, and globally competitive AI capabilities. This research/project is supported by the National Research Foundation, Singapore under its National Large Language Models Funding Initiative (AISG Award No: AISG-NMLP-2024-002). 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.
* This research/project is supported by the National Research Foundation, Singapore under its National Large Language Models Funding Initiative (AISG Award No: AISG-NMLP-2024-002). 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.

Principal Investigator: Pang Guansong
Affiliation of PI: School of Computing and Information Systems
Funding Source: Agency for Science, Technology and Research's Individual Research Grants & Young Individual Research Grants
Project Synopsis: Aircraft surface inspection is typically done in an open environment, where the inspection model can be challenged by a lack of annotated defect samples, incomplete knowledge about possible defect types, and varying nature conditions or surface appearance. Vision-and-language foundation models have shown unique advantages in handling these challenges in various vision tasks. This project aims to develop innovative approaches to adapt such foundation models for addressing these challenges in aircraft surface inspection. The resulting models will help largely reduce aircraft maintenance cost and alleviate the risk of having unnoticed defects due to time/manpower limitation in aircraft inspection in Singapore.

Principal Investigator: Zhou Pan
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Academic Research Fund Tier 2
Project Synopsis: This proposed project, FoCo, aims to develop Fast, cOmmunication-, and memory-effiCient Optimizers, specifically targeting the main identified problems of currently popular optimizers like Adam and AdamW in training large AI models, as follows: a. FoCo will develop faster optimizers to reduce the training time and thus the cost of large AI models. b. FoCo will design an Adaptive and COMpensate compression Approach called Acoma to reduce the communication costs of our faster optimizer and other optimizers like Adam. c. FoCo will develop a memory-efficient approach called MeMo to lower the GPU memory of our faster and communication-efficient optimizer and other optimizers like Adam. Although FoCo’s three main objectives focus on different aspects of training large AI models, they all work towards the common goal of making large AI training more efficient and faster. Improvements in one area will positively impact the others. Given the increasing importance and widespread use of large AI models, addressing their current training challenges is crucial. High training costs, long development times, and significant energy consumption and emissions are major concerns. By making AI training more efficient, FoCo will not only advance the field of AI but also contribute to a more sustainable and resource-efficient future. This project will benefit academia, industry, and society by enabling faster and more cost-effective development of advanced AI technologies.

Principal Investigator: Tan Ah Hwee
Affiliation of PI: School of Computing and Information Systems
Funding Source:
Project Synopsis: This project shall (i) enhance the generalizability of hierarchical multi-agent learning and control framework for heterogeneous agents in a range of scenarios and (ii) develop algorithms to analyse and explain the learned behaviour models at the various levels.

Principal Investigator: Rajesh Krishna Balan
Affiliation of PI: School of Computing and Information Systems
Funding Source: Smart Nation Group's Translational R&D 2.0 Grant
Project Synopsis: In this project, the problem being addressed is to provide an accurate, low-latency, minimal maintenance indoor localisation solution to locate organisational resources. Our goal is to achieve this without any form of Wi-Fi fingerprinting, without any knowledge of the location of the Wi-Fi Access Points (AP), and without the availability of any maps of the indoor spaces being used. We plan to achieve this by leveraging the new 802.11mc Wi-Fi Fine-Time Measurement standard in pure 1-sided mode that allows time-of-flight measurements to be made between a client device and any AP. These measurements will then be used with inertial data to jointly optimise both the location of the device and the location of the APs.

Principal Investigator: Gao Wei
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme Grant Call 2024
Project Synopsis: The proposed project studies how to automate fact-checking (FC) based on neural language models. FC is the investigative process of verifying and reporting the accuracy of claims to help people make decisions based on facts rather than misinformation. Our proposed project, named FrankLM, targets Fact-check and report automation via neural knowledge Language Modeling to enable the applicability of LLMs for FC. We aim to improve task accuracy by 20% for claim verification and explanation generation, improve task accuracy by 15% for reasoning, and achieve over 90% of human performance in report generation. FrankLM will benefit FC and improve the accuracy, explainability, and trustworthiness of AI systems, and it will open new opportunities to apply them in various sectors including media, healthcare, finance, and education. This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG3-RP-2024-035).
* This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG3-RP-2024-035).

Co-Principal Investigator: Xie Xiaofei
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: CyberSG R&D Programme Office
Project Synopsis: In this proposal, we consider a real-world setting that a large model trainer like OpenAI already holds large-scale training data, but it continuously needs more fresh data to update the model or produce specific downstream tasks. Such data sharing mechanism benefits both the model trainer and data provider who should be paid for his contribution. This motivates us to achieve the following four key objectives: (1) Private pre-processing of training data sharing for large model; (2) Secure and private regulation compliance inspection on shared training data with verification and proof; (3) Privacy-preserving dynamic fine-grained training data sharing; and (4) Privacy-preserving inference on large models.

Principal Investigator: David Lo
Affiliation of PI: School of Computing and Information Systems
Funding Source: Agency for Science, Technology and Research
Project Synopsis: This research project, developed under the CSIRO - A*STAR Research-Industry (2+2) Partnership Program, aims to develop sustainable and responsible AI technologies, with a particular focus on large language models (LLMs). The project's objective is twofold: enhancing environmental sustainability and ensuring compliance with governance standards.

Principal Investigator: Lim Ee Peng
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore Ministry of Health through the National Medical Research Council (NMRC) Office, MOH Holdings Pte Ltd
Project Synopsis: The project conducts research on new conversational AI technologies that understand a user’s mental health conditions and enable a principled strategy to counsel the user. The research will focus on incorporating user personalisation and counselling strategies into the AI models. At the end of project, we hope to create a conversational AI framework that can automate mental health counselling and evaluate its performance.

Principal Investigator: Ma Dong
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Academic Research Fund Tier 2
Project Synopsis: In the dynamic field of wearable health technology, our proposed research aims to revolutionise how we monitor our health using devices such as smartwatches and earbuds. These devices frequently employ photoplethysmography (PPG), a noninvasive technique that monitors changes in blood volume under the skin, providing valuable insights into cardiovascular health. However, real-world challenges, such as inaccuracies during physical activities and the impact of diverse body postures, impede the realisation of the full potential of PPG technology with respect to these wearable devices. Our research focuses on a breakthrough concept: incorporating contact pressure (CP) into PPG measurements to address the aforementioned challenges. By analyzing the tightness of wearable devices against the skin, we aim to obtain valuable insights that can help reconstruct high-quality PPG data from the noisy PPG data. Our first contribution is the development of a wearable prototype capable of concurrently measuring CP and PPG. Using this prototype, we will develop intelligent algorithms to mitigate the effects of physical activities and body postures. Finally, we will optimise our energy efficiency and real-time processing methodologies, ensuring prolonged battery life for wearable devices. We believe that these innovations can substantially improve the accuracy and reliability of health data obtained from wearables, thereby unlocking new capabilities of PPG in health monitoring. The success of this research has the potential to stimulate market growth by establishing a new standard for accuracy and capabilities in wearable devices. Considering the global aging population, our research will considerably impact elderly care, particularly cardiovascular diseases. By improving the reliability of wearable devices, our research can promote an active lifestyle and contribute to overall well-being among the general population.

Principal Investigator: Archan Misra
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Academic Research Fund Tier 2
Project Synopsis: Artificial intelligence (AI)-driven personal assistants like Apple’s Siri and Amazon’s Alexa have gained significant popularity due to their user-friendly natural-language interfaces that facilitate interaction with the Internet. These agents, embedded in various devices, assist users with tasks such as retrieving information, controlling home automation, and managing calendars. However, current assistants lack awareness of the physical environment; they cannot connect directly to sensors to interpret a user's physical environment, limiting their ability to respond to contextual queries, such as identifying the manufacturer of a toaster pointed out by a user. Recent advances in AI, particularly with models like GPT-4, have enhanced capabilities for machine-based reasoning that combine visual, verbal, and gestural inputs. Projects like Google’s Project Astra are exploring these multi-modal assistants, which could revolutionize human-computer interactions. Additionally, the integration of sophisticated sensors in mobile and IoT devices, such as LIDAR technology in smartphones, opens the door for new situated agents that can leverage spatial awareness and multimodal reasoning to deliver contextually relevant information. Developing these situated agents presents significant challenges, notably in energy consumption and computational complexity. High-power sensors like LIDAR are unsuitable for continuous use, and current AI models are too large to run efficiently on mobile devices. To address these issues, the project proposes innovations in energy-efficient sensing techniques and optimization of deep neural networks (DNNs) to minimize latency and energy use. Through innovations in triggering novel spatial sensors on-demand, processing relevant multi-modal sensor data selectively and by exploiting the cached results of recent reasoning actions, these advancements aim to enable responsive and pervasive situated agents that can interact meaningfully with users. The project also seeks to embed these innovations in a prototype conversational agent that can serve as a real-time tutor, responding intelligently to a learner’s queries and enhancing educational experiences.

Principal Investigator: Hu Nan
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Academic Research Fund Tier 2
Project Synopsis: In recent years, the rapid advancement of Large Language Models (LLMs) has driven significant progress in Generative Artificial Intelligence (GAI). By emulating human language capabilities, GAI has unlocked a multitude of applications, ranging from chatbots and virtual assistants to translation services and content-generation tools. However, GAI has evolved into a double-edged sword, giving rise to ethical concerns surrounding transparency, privacy protection, misinformation, bias and fairness, job displacement, and environmental impact. In this project, the team aims to quantify and validate firms' ethical awareness of GAI and employ econometrics models and quasi-experiments to comprehend its determinants and document consequences. Through these efforts, the project makes a distinctive contribution to the promising trajectory of developing responsible and ethical GAI systems in business, ultimately fostering a sustainable society.

Principal Investigator: Lim Ee Peng
Affiliation of PI: School of Computing and Information Systems
Funding Source: National University of Singapore
Project Synopsis: (This is additional funding to SMU for the existing research project.) Digital wellbeing has arisen in public, governmental and policy discourse as a key measure of a person’s wellbeing through a healthy use of technology. This project aims to identify and measure digital wellbeing for digital readiness, inclusion and safety.

Principal Investigator: Yang Guomin
Affiliation of PI: School of Computing and Information Systems
Funding Source: Digital Trust Centre Research Grant Call
Project Synopsis: A trusted digital identity is an essential component for securely and conveniently accessing services and authorizing transactions in cyberspace. With the rapid development of decentralized technologies and applications, such as distributed ledgers, Web3, and decentralized finance, there is an urging demand for decentralized digital identities (DID), also known as self-sovereign identities, which empower end users to create, own and govern their digital identities and assets in an autonomous, reliable, and privacy-preserving manner. The overarching goal of this project is to develop and implement a trusted, versatile, reliable and user centric DID framework covering a complete DID lifecycle. Specifically, the project aims to investigate novel techniques for enabling key components and features that are either missing or inadequately addressed in the existing DID proposals. This research is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Trust Tech Funding Initiative.

Principal Investigator: Archan Misra
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore-MIT Alliance for Research and Technology
Project Synopsis: This project focuses on “human capital development” that involves, as one of its core pillars, the ability to use AI to improve learning. Through this project, the SMU Research team will work with the MIT team to build up key AI based models for supporting interactive learning by supporting natural multi-modal question answering associated with learning tasks. The SMU team will also develop initial prototypes that embed such interactive learning in online learning platforms.

Principal Investigator: Archan Misra
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore-MIT Alliance for Research and Technology
Project Synopsis: This project focuses on “immersive workplaces” that involve extensive and interactive co-working by humans and AI-enhanced agents/robots. The research will investigate techniques to combine the latent cognitive state of a human worker, with explicit instructions issued by humans using a natural mix of visual, verbal and gestural cues, to build powerful new capabilities for human-robot co-working in immersive workplaces and industrial settings. The SMU Research team will develop optimized AI models, as well as prototype pervasive systems, that allow a variety of robotic agents to disambiguate, comprehend and respond to commands issued by human workers.

Co-Principal Investigator: Cao Zhiguang
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Synopsis: In this project, we aim to develop a generic framework to coordinate human and AI for tackling practical decision-making problems, e.g., supply chain delivery planning and healthcare scheduling, for improving the operational effectiveness and efficiency of activities in different scenarios. We will develop various cutting-edge machine learning methods to build reliable, generalizable, and explainable AI models to assist human decision making in various complex and large-scale contexts.

Principal Investigator: Lim Ee Peng
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Academic Research Fund Tier 2
Project Synopsis: In this project, we propose to use a computational social science approach to analyse career trajectories using very large resume datasets combining social science with AI methods (i.e., Large Language Model-based methods augmented by knowledge graphs). The objective is to perform fine-grained analysis of career trajectory data determining the factors that contribute to career mobility as well as factors that prevent career mobility. Our new computational social science approach can be reused for future follow-up studies to reveal other detailed career trends and patterns. Hopefully, our new proposed work will also detect early signals on types of career trajectories and skills among specific demographic groups, as well as emerging trends that threaten employment, career progression and wellbeing of the workforce.

Principal Investigator: Lee Min Hun
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Academic Research Fund Tier 2
Project Synopsis: Recent advancements in AI make it possible to process large amounts of medical imaging data and replicate clinicians’ decisions with competitive performance. However, the adoption of AI in clinics has been challenging due to several issues, such as clinicians’ inability to understand how AI operates to trust and adopt it in practice. In this project, we aim to develop and evaluate a human-AI collaborative system and practices for improving collaboration between clinicians and AI in the context of head and neck cancer screening. This system learns representations of clinical videos to identify urgent referral cases and generates AI explanations on interactive visualizations to improve clinicians’ understanding of AI and their practices. After implementing the proposed system, we will conduct user studies to evaluate the effectiveness of the system.

Principal Investigator: Xie Xiaofei
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Academic Research Fund Tier 2
Project Synopsis: The main goal of this project is to develop new technologies to test how well the perception module of an autonomous driving system functions and understand how perception errors impact other parts of the system, like decision-making. The project team aims to create innovative solutions to evaluate the performance of the perception module in autonomous driving. Throughout the project, the team will utilize software testing technologies, machine learning technologies, formal methods, and evolutionary algorithms to explain and develop their methods. The resulting technologies will contribute to improving the safety and security of autonomous vehicles from their development phase to actual use on the road.

Principal Investigator: Wang Hai
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore-MIT Alliance for Research and Technology
Project Synopsis: 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 will also be explored.

Principal Investigator: Shim Kyong Jin
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Tertiary Education Research Fund
Project Synopsis: This research project aims to leverage Virtual Reality (VR) and Artificial Intelligence (AI) to improve public speaking skills through immersive, real-world scenario simulations. The project seeks to develop a VR system with AI-driven avatars that respond dynamically to a presenter’s body language and speech, enhancing the learning experience by providing interactive and personalized feedback. It addresses the scalability and resource limitations of traditional public speaking training by offering a virtual environment where students can practice and refine their skills without the need for a physical audience. The research will explore PresentationPro's effectiveness in helping students achieve learning outcomes in university public speaking programs and equip them with key skills for the future workplace. By incorporating advanced AI, machine learning, and VR technologies, PresentationPro aims to provide a realistic and accessible virtual practice experience that reduces public speaking anxiety and improves performance. The project will be assessed through pilot studies focusing on learning outcomes, system usability, and the immediate applicability of training in real-world settings.

Principal Investigator: Ouh Eng Lieh
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education Tertiary Education Research Fund
Project Synopsis: The objective of this project is to enhance students’ comprehension, retention, and overall learning outcomes in programming by leveraging AI-enabled PromptTutor. It aims to design an AI-enabled intervention that prompts students to reflect on their completed tasks, address doubts in their reflections, and provides additional learning resources in a personalised and timely manner.

Principal Investigator: He Shengfeng
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore's AI Governance Research Grant Call
Project Synopsis: In this digital age, advancements in artificial intelligence (AI) have brought about both great opportunities and significant challenges. One of these challenges revolves around the protection of personal data, particularly digital images, which can be exploited by AI technologies. The proposal focuses on addressing these issues by developing solutions that can safeguard the digital rights of individuals and protect their creations from potential misuse by AI technologies. It offers a 'cloak of invisibility' to your digital images, rendering them unexploitable by AI while retaining their visual appeal for human observers. The project aims to return control to the individuals, ensuring the protection of their art and their privacy in the digital world. This research/project is supported by the National Research Foundation, Singapore under the AI Singapore Programme (AISG Award No: AISG3-GV-2023-011).
* This research/project is supported by the National Research Foundation, Singapore under the AI Singapore Programme (AISG Award No: AISG3-GV-2023-011).

Principal Investigator: Zhu Feida
Affiliation of PI: School of Computing and Information Systems
Funding Source: Zeasn Technology Pte Ltd
Project Synopsis: ZEASN Technology is a global leader in smart TV solutions since 2011, and it is headquartered in Singapore with a strong global presence. ZEASN's flagship product, Whale OS, powers 90 million devices globally for over 300 brands. The collaborative research between SMU and ZEASN Technology Pte Ltd is dedicated to developing an advanced Web 3.0 creative media content ecosystem. Emphasizing critical aspects like tokenomics, incentive design, and privacy-enhancing computation, the project’s our primary goal is to construct a future-proof digital framework that is user-friendly, secure, and maximizes user participation, privacy, and profit. Anticipated outcomes include a robust, efficient, and scalable Web 3.0 creative media content ecosystem, maintaining user privacy while fostering a dynamic, tokenomics-driven creative space. This comprehensive approach seeks to revolutionize how creative media is created, shared, and monetized, empowering users and content creators in the digital era. Leveraging combined expertise from economics, computer science, and digital media, the team we aim to design an ecosystem aligned with the values of the Web 3.0 vision: decentralized, user-centric, and privacy-preserving. An early harvest of this collaboration is addressing key challenges in the century-old film industry, with plans for a Web3-powered virtual cinema on ZEASN's worldwide Whale OS CTVs, aiming to decentralize film distribution and monetization in a transparent and rewarding fashion.

Principal Investigator: WANG Hai
Affiliation of PI: School of Computing and Information Systems
Funding Source: Tokka Labs Pte Ltd
Project Synopsis: The global fintech landscape is undergoing a pivotal shift at its core, driven in part by advanced AI techniques. This project aims to: (i) understand the inner workings of diverse investment systems to assess their transaction patterns; (ii) create algorithms that decode fintech data, offering insights and aiding in market behavior predictions; and (iii) leverage optimization and AI methods to enhance trading and transaction systems.

Principal Investigator: Tan Ah Hwee
Affiliation of PI: School of Computing and Information Systems
Funding Source: Sengkang General Hospital Pte Ltd
Project Synopsis: 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.

Principal Investigator: Zhu Feida
Affiliation of PI: School of Computing and Information Systems
Funding Source: Slowmist Pte Ltd
Project Synopsis: 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.

Principal Investigator: Lau Hoong Chuin
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore
Project Synopsis: 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).
* This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-100E-2023-118).

Principal Investigator: Liao Lizi
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: 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.

Co-Principal Investigator: Xie Xiaofei
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: AI Singapore
Project Synopsis: 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.

Principal Investigator: David Lo
Affiliation of PI: School of Computing and Information Systems
Funding Source: National Research Foundation
Project Synopsis: This project will pioneer approaches that realize trusted automation bots that act as concierges and interactive advisors to software engineers to improve their productivity as well as software quality. TrustedSEERs will realize such automation by effectively learning from domain-specific, loosely-linked, multi-modal, multi-source and evolving software artefacts (e.g., source code, version history, bug reports, blogs, documentation, Q&A posts, videos, etc.). These artefacts can come from the organization deploying the automation bots, a group of collaborating yet privacy-aware organizations, and from freely available yet possibly licensed (e.g., GPL v2, GPL v3, MIT, etc.) data contributed by many, including untrusted entities, on the internet. TrustedSEERs will bring about the next generation of Software Analytics (SA) – a rapidly growing research area in the Software Engineering research field that turns data into automation – by establishing two initiatives: First, data-centric SA, through the design and development of methods that can systematically engineer (link, select, transform, synthesize, and label) data needed to learn more effective SA bots from diverse software artefacts, many of which are domain-specific and unique. Second, trustworthy SA, through the design and development of mechanisms that can engender software engineers’ trust in SA bots considering both intrinsic factors (explainability) and extrinsic ones (compliance to privacy and copyright laws and robustness to external attacks). In addition, TrustedSEERs will apply its core technologies to synergistic applications to improve engineer productivity and software security.

Principal Investigator: Jiang Jing
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: Consumers have widely used conversational AI systems such as Siri, Google Assistant and now ChatGPT. The next generation of conversational AI systems will have visual understanding capabilities to communicate with users through language and visual data. A core technology that enables such multimodal, human-like AI systems is visual question answering and the ability to answer questions based on information found in images and videos. This project focuses on visual question answering and aims to develop new visual question-answering technologies based on large-scale pre-trained vision-language models. Pre-training models developed by tech giants, particularly OpenAI, have made headlines in recent years, e.g., ChatGPT, which can converse with users in human language, and DALL-E 2, which can generate realistic images. This project aims to study how to best utilise large-scale pre-trained vision-language models for visual question answering. The project will systematically analyse these pre-trained models in terms of their capabilities and limitations in visual question answering and design technical solutions to bridge the gap between what pre-trained models can accomplish and what visual question answering systems require. The end of the project will be a new framework for building visual question-answering systems based on existing pre-trained models with minimal additional training.

Principal Investigator: Wang Yong
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: Data visualisations have been widely used on mobile devices (e.g., smartphones), but they suffer from mobile-friendly issues in terms of their creation and usage. This project aims to develop novel techniques to achieve mobile-friendly data visualisations, including desirable mobile data visualisation creation and effective multimodal interaction design. The research outputs of this project will significantly improve the effectiveness and usability of mobile data visualisations and further promote their applications.

Principal Investigator: Ngo Chong Wah
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: This project aims to improve the scalability of food recognition – to train classifier(s) that recognise a wide range of dishes regardless of cuisines, the amount and type of training examples. Here, “classifier” can be viewed as a “search engine” that retrieves the recipe of a food image. Training such classifiers requires an excessive number of training examples composed of recipes and images, where each recipe is paired with at least an image as visual reference. Training classifiers using paired or parallel data faces several practical limitations – tens of thousands of recipe-image pairs are required for training; other forms of data that are largely available in the public cannot be leveraged for model training; and additional training data is required when the recipes are written in different natural languages. Through the project, these practical limitations will be addressed from the perspective of transfer learning. The aim is to train a generalised classifier that is more adaptable for recognition, by removing the statistical bias, considering the evolving process, and aligning the semantics of different languages in machine learning.

Principal Investigator: Sun Jun
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: This project aims to provide a solid foundation for analysing AI systems as well as techniques used to facilitate the development of reliable secure AI systems. Central to the research is to develop an executable specification in the form of an abstract logical representation of all components that are used to build artificial intelligence, which subsequentially enables powerful techniques to address three problems commonly encountered in AI systems, namely, how to ensure the quality or correctness of AI libraries, how to systematically locate bugs in neural network programs, and how to fix the bug. In other words, this project aims to define a semantics of AI models, thereby forming a solid fundamental to build AI systems upon.

Principal Investigator: Jiang Jing
Affiliation of PI: School of Computing and Information Systems
Funding Source: DSO National Laboratories
Project Synopsis: Text style transfer (TST) is the task of converting a piece of text written in one style (e.g., informal text) into text written in a different style (e.g., formal text). It has applications in many scenarios such as AI-based writing assistance and removal of offensive language in social media posts. Recent years, with the advances of pre-trained large-scale language models such as the Generative Pre-trained Transformer 3 (GPT-3) which is an autoregressive language model that uses deep learning to produce human-like text, solutions to TST are now shifting to fine-tuning-based and prompt-based approaches. In this project, we will study how to effectively utilize pre-trained language models for TST under low-resource settings. We will also design ways to measure whether solutions based on pre-trained language models can disentangle content and style.

Principal Investigator: Sun Qianru
Affiliation of PI: School of Computing and Information Systems
Funding Source: DSO National Laboratories
Project Synopsis: This project aims for learning efficient semantic segmentation models without using expensive annotations. Specifically, we leverage the most economical image-level labels to generate pseudo masks to facilitate the training of segmentation models. In the end, we will apply the resultant algorithms on tackling the remote sensing image segmentation in the challenging Continual, Few-shot, and Open-set Datasets.

Co-Principal Investigator: Zhu Feida
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: Info-communications Media Development Authority of Singapore
Project Synopsis: 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.

Principal Investigator: Lau Hoong Chuin (SMU PI)
Affiliation of PI: School of Computing and Information Systems
Funding Source: National Research Foundation
Project Synopsis: 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.

Principal Investigator: Lau Hoong Chuin
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore
Project Synopsis: 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 research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-100E-2022-098).

Principal Investigator: Fang Yuan
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: 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.

Co-Principal Investigator: Lim Ee-Peng
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: National University of Singapore
Project Synopsis: Digital wellbeing has arisen in public, governmental and policy discourse as a key measure of a person’s wellbeing through a healthy use of technology. This project aims to identify and measure digital wellbeing for digital readiness, inclusion and safety. Building on the Digital Wellbeing Indicator Framework (DWIF) developed by researchers at the NUS Centre for Trusted Internet and Community, this project will test, evaluate, and revise the DWIF by conducting both qualitative and quantitative analysis of data collected from local context (i.e, Singapore) and global contexts (ie, UK, US, China), with specific focus on mainstream job trends (digital readiness), minority disability access (digital inclusion) and women (digital safety).

Principal Investigator: Lauw Hady Wirawan
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Synopsis: A recommender system presents a personalized experience to each user. One perennial issue affecting current recommendation technologies is the sparsity of data related to user preferences. The overall objective of this proposed research is to address this sparsity problem by a combination of approaches that together enable lifelong learning for recommender systems. This is done by allowing the recommendation model to evolve over time to new users and items and, to transfer over to new product categories. In addition, the proposed recommendation model would have the ability to cross from a source platform that accumulates longer-term preferences to a target platform that seeks to integrate short-term signals and reinforcement learning. This provides a system that is able to learn from longer-term preferences and provide the necessary flexibility for cross platform applications.

Principal Investigator: Lauw Hady Wirawan
Affiliation of PI: School of Computing and Information Systems
Funding Source: MOE Tertiary Education Research Fund
Project Synopsis: This research is an ideation/proof of concept project to develop an interactive Web application, called Slide++, which allows students to self-explore additional content related to their courses, while still being directed by the lesson materials provided by an instructor. Importantly, its primary feature is to provide content augmentation for every slide in the form of learning resources relevant to the slide being viewed. These resources can be of various modalities, including Web pages, videos, or questions and answers (Q&A’s).

Principal Investigator: Ta Nguyen Binh Duong
Affiliation of PI: School of Computing and Information Systems
Funding Source: MOE Tertiary Education Research Fund
Project Synopsis: This project aims to build an Automatic Programming Coaching system that is based on a combination of AI and software engineering techniques to support students practice coding via formative feedback generation.

Co-Principal Investigator: Griffin Paul Robert
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: Quantum Engineering Programme
Project Synopsis: Rare events, also known as “black swans”, in financial time series can be seen as sporadic and drastic jumps in financial assets returns. Accurate and timely estimates of future risk associated with rare events are of great importance for finance practitioners, policymakers, and regulators. The research team will leverage the most recent developments in quantum-enhanced Monte-Carlo sampling, stochastic modelling and dimensional reduction to design a set of quantum algorithms for rare event estimation that: 1. Enhance the accuracy in estimating the probability of specific rare events – we anticipate a quadratic scaling improvement, where doubling the iterations for the quantum algorithm will result in an accuracy improvement equivalent to a quadrupling of iterations in its classical counterpart. 2. Reduce systematic error caused by dimensional reduction – when constrained to storing the same amount of past data (e.g., macroeconomic indicators), our quantum model can give more accurate rare event predictions than classical counterparts.

Principal Investigator: Lau Hoong Chuin
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore 100 Experiments Programme
Project Synopsis: This is a project under the AI Singapore 100 Experiments Programme. It tackles the research challenge of generating good logistics plans and schedules for parcel delivery using AI. uParcel faces the challenges that the number of deliveries daily are in the thousands and the number of drivers delivering a day is in hundreds, which makes it very challenging to match jobs to drivers and encourage job acceptance. Using reinforcement learning coupled with large-scale optimization methods, the research team will develop route optimization, dynamic recommendation, and logistics marketplace matching algorithms for improving operational efficiency. This will also greatly improve city logistics by reducing trips and congestion.

Principal Investigator: Kyriakos Mouratidis
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: With the advent of e-commerce, users are presented with numerous alternatives to satisfy their everyday needs. Choosing from the available options generally entails the consideration of multiple, often conflicting aspects, the tradeoff among which is assessed differently by different users. This project proposes PERFLEXO, a new methodology for multi-objective querying centred around three hard requirements, i.e., personalization, flexibility in the preference input, and output-size control. Past approaches have considered these requirements individually, but no existing work satisfies all three of them. On the technical side, the main contributions of the project will centre on PERFLEXO’s ability to process large option-sets (i.e., scalability) and produce shortlists in reasonable time (i.e., responsiveness).

Principal Investigator: Sun Qianru
Affiliation of PI: School of Computing and Information Systems
Funding Source: Alibaba DAMO Academy (Hangzhou) Technology Co., Ltd’s Alibaba Innovative Research Programme
Project Synopsis: AI models trained offline rely on the accessibility of all classes in training data. When they are updated online to learn new incoming data, they often bias to the patterns of new classes, and thus forget old ones. The problem is known as catastrophic forgetting. This project aims to tackle this issue by task-specific data augmentation. The augmentation for old classes is achieved by distilling from new or open-set data that contain the knowledge of old classes, e.g., shared contexts and sub-parts.

Principal Investigator: Lau Hoong Chuin
Affiliation of PI: School of Computing and Information Systems
Funding Source: Fujitsu Laboratories Ltd
Project Synopsis: (This is a 6-month extension of the research collaboration with Fujitsu Ltd.) Under the Fujitsu-SMU Urban Computing and Engineering (UNiCEN) Corp Lab, SMU has undertaken the Digital Platform Experimentation (DigiPlex) project with Fujitsu. The project was carried out using the Digital Annealer (DA), a quantum inspired-technology inspired by Fujitsu. Through the DigiPlex project, certain challenges in solving constrained optimization problems using such technology, and promising methods on tuning of the underlying model parameters to improve run time performance, have been identified. This project aims at developing hyper parameter tuning methodology, machine learning techniques, operations research algorithms, and software tools to enhance quantum-inspired techniques for solving large scale real-world combinatorial optimization problems.

Principal Investigator: Robert Deng
Affiliation of PI: School of Computing and Information Systems
Funding Source: Huawei International Pte Ltd
Project Synopsis: The world is experiencing a rapid transition towards a digital society. Although huge number of Internet of Things (IoT) devices are being deployed to provide accurate and real-time sensing and observation of the environment, security and privacy concerns are becoming one of the major barriers for large scale adoption and deployment of IoT. To that end, this project aims to provide IoT devices with privacy-aware authentication and flexible authorisation capabilities to build trust in IoT.

Principal Investigator: Shar Lwin Khin
Affiliation of PI: School of Computing and Information Systems
Funding Source: National Satellite of Excellence - Mobile Systems Security and Cloud Security Research Programme RFP
Project Synopsis: With the widespread adoption of drones in civilian, business, and government applications nowadays, concerns for breaches of safety, security, and privacy by exploiting drone systems are also rising to the highest national level. Malicious entities have used drones to conduct physical and cyber-attacks such as unauthorized surveillance, drug smuggling, armed use, etc. In this project, the research team aims to develop methods and tools for analysing a list of drones to audit drones for detecting anomalies such as malware, data leak, software bugs that could be exploited to conduct criminal/malicious drone activities. The research team will analyse at least five different drone-related criminal/malicious activities from their collaborator and demonstrate how ADrone can assist Drone forensic analysts with the detection of the root causes of activities.

Principal Investigator: Kotaro Hara
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: In this Human-Computer Interaction research, the research team will design a novel system that addresses the low wage of online crowd work—also known as online gig-economy. By using knowledge from mechanism design in the economics literature, the research team will design and develop user interfaces through machine learning models that: Present information to encourage crowdsourcing requesters pay a fairer wage to online workers; and Use nudging messages and information visualization to persuade workers to submit high-quality work.

Principal Investigator: Lau Hoong Chuin
Affiliation of PI: School of Computing and Information Systems
Funding Source: IBM Manufacturing Solutions Pte Ltd
Project Synopsis: This research collaboration with IBM aims to develop the optimisation capabilities to build a cutting-edge resilient supply chain, leveraging data science to preserve the continuity and consistency of product supply and meet business obligations for product delivery and service to customers in the face of both short-term operational and longer-term strategic disruptions. In this project, the team seeks to leverage IBM’s relevant internal, supplier-provided, public and subscription data sources to improve operational decision-making capability to proactively anticipate and respond to disruptive events, and to enable resiliency evaluations for products, product families, or tiered supply networks.

Principal Investigator: Cheng Shih-Fen
Affiliation of PI: School of Computing and Information Systems
Funding Source: Mercurics Pte Ltd
Project Synopsis: The "SmartBFA 2.0" project aims to build a "Google Maps" equivalent for wheelchair users, so that they can find barrier-free access paths when navigating around Singapore. This objective is in line with Singapore's vision towards building a smart and inclusive city for everyone. A major innovation of the research team's project is the incorporation of crowdsourced sensor inputs; in particular, they aim to solicit multi-modal data collected from a smartphone app to supplement the accessibility information that they have collected using specially-designed sensors. They also seek to collect user feedback, so as to make their system more useful to wheelchair users.

Principal Investigator: Cheng Shih-Fen
Affiliation of PI: School of Computing and Information Systems
Funding Source: MOE Social Science Research Thematic Grant
Project Synopsis: "Learning by doing” (LBD) is the phenomenon where a worker’s productivity rises with cumulative production experience. As LBD requires no additional investment in hiring or equipment investment, it is viewed by many as an important channel for firms to achieve productivity growth. Unfortunately, although conceptually simple and intuitive, the sources and enablers of LBD remain a mystery; as a result, even when a firm intends to facilitate LBD among its employees, it is not clear how to effectively achieve it. This challenge originates from the difficulty in quantifying and isolating the effects of LBD, and even in a few instances where the measurement of LBD effects (in terms of productivity) is made possible by natural events, these measurements are typically only at the aggregate level. In this project, the team aims to build a novel Big Data framework to measure the LBD effects for workers in the transport gig economy in Singapore. Their ambition is to measure LBD effects at not just the productivity level, which is easily tainted by other factors, but also at the skill level. They plan to achieve this by mining drivers’ microscopic movement traces and trip fulfilment (including both taxi and ride-hailing drivers), and quantify drivers’ skills in anticipating demands and competition from other drivers. Their research will provide a rare view into how big data can revamp the understanding of labor productivity and LBD effects at the individual level, and it will help policy makers and platform operators to come up with policies that are more effective in helping workers cope with competitions and sudden changes such as disruptions brought about by the COVID-19 pandemic.

Principal Investigator: Sun Jun
Affiliation of PI: School of Computing and Information Systems
Funding Source: MOE Academic Research Fund (AcRF) Tier 3
Project Synopsis: In this multi-pronged initiative, we propose to build a framework for developing certifiable AI systems systematically, i.e. with the help of theories, tools, certification standards and processes. This is motivated by the many recently discovered problems on existing AI techniques and systems, e.g. adversarial samples, privacy and fairness issues, as well as the many ad hoc attempts on fixing them. For AI techniques to truly become part of a wide digital transformation across many industries, it is vital that we have foundational mechanisms to quantify the problems in AI models, and rectify the discovered problems.

Principal Investigator: Lau Hoong Chuin
Affiliation of PI: School of Computing and Information Systems
Funding Source: Y3 Technologies Pte Ltd
Project Synopsis: This project aims at developing a software engine that enables planners to generate optimised routing plans based on a given set of data inputs subject to constraints. This work is an offshoot from the Collaborative Urban Delivery Optimisation (CUDO) project which was completed under the Fujitsu-SMU Urban Computing and Engineering (UNiCEN) Corp Lab, that was funded by the National Research Foundation (NRF). The project will customise and extend the functionalities of the CUDO engine developed at SMU, and integrate the engine into Y3 Technologies’ enterprise platform system via an Application Programming Interface (API).

Principal Investigator: Fang Yuan
Affiliation of PI: School of Computing and Information Systems
Funding Source: Agency for Science, Technology and Research (A*STAR) AME Programmatic Funds
Project Synopsis: Deep learning has enabled significant advances in applications involving image, text and audio data, in applications such as surveillance, machine translation and speech recognition. These successes have positioned deep learning as a promising approach to address critical challenges (reducing defects, design-time and down-time) in the advanced manufacturing and engineering (AME) domain. A major barrier to this broader application of deep learning is its need for large, labeled datasets to obtain good performance. Thus, the team aims to develop novel deep learning methods that can learn with 10 to 100 times less data as compared to current approaches. This project will enable deep learning to be used in a wider range of applications especially where data is scarce or expensive to obtain. The team will also demonstrate their methods using real-world data from the three identified AME applications (tentatively defect identification, predictive maintenance and circuit design) to show progress and applicability to the AME domain. This project is a collaboration between A*STAR’s I2R, SMU, SUTD, NTU and NUS.

Co-Principal Investigator: Pradeep Varakantham
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Synopsis: Through the project, the team proposes an integrative program of fundamental research towards a vision in which every human will have such an AI assistant for daily life and work. The overall aim is to build conceptual understanding of human-AI collaboration, to develop representations, models, and algorithms for situated assistance, and to integrate them in an experimental device platform for evaluation. The research program consists of five thrusts: (i) situated language communication with reasoning, (ii) visual-linguistic situation understanding, (iii) human collaboration modeling, (iv) robust situated teaming, and (v) the integrative showcase project The “Other Me”, which develops and evaluates the experimental platform Tom – a wearable situated AI agent that assists the human in creating novel artifacts or diagnosing faults. The research aims to empower a new generation of AI assistants for situated, just-in-time, and federated assistance. They will reinvent the relationship between the human and the device in our daily life and work. This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-016). 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.

Principal Investigator: Tan Ah Hwee
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Synopsis: As AI becomes ubiquitous, people’s trust in AI is actually dwindling. The key barrier to adopting AI is no longer technical in nature, but more about gaining stakeholders’ trust. Federated Learning (FL), in which training happens where data are stored and only model parameters leave the data silos, can help AI thrive in the privacy-focused regulatory environment. FL involves self-interested data owners collaboratively training machine learning models. In this way, end-users can become co-creators of AI solutions. To enable open collaboration among FL co-creators and enhance adoption of the federated learning paradigm, this project aims to develop the Trustworthy Federated Ubiquitous Learning (TrustFUL) framework, which will enable communities of data owners to self-organize during FL model training based on three notions of trust: 1) trust through transparency, 2) trust through fairness and 3) trust through robustness, without exposing sensitive local data. As a technology showcase, we will translate TrustFUL into an FL-powered AI model crowdsourcing platform to support AI solution co-creation.

Principal Investigator: Pradeep Varakantham
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Synopsis: In safety-critical applications (e.g., where human lives are at stake), it is crucial for humans to be well-trained to handle expected and unexpected scenarios of varying complexity. Paramedics frequently respond to life-or-death situations, and they must be trained to handle expected and unexpected situations with respect to patient condition and response to treatment effectively. Manned vessels in maritime traffic operate in environments with many other vehicles, and humans must be trained to safely navigate varied situations. A wide range of critical activities in crime response, healthcare, defence, and construction also require training to improve safety. Through this project, the team intends to develop and assess Explainable and tRustworthy (ExpeRt) AI (or Agent) Training Programs (ATPs) with feedback interfaces to adaptively train human(s) for safety-critical applications with showcase projects on emergency response and maritime navigation. The ExpeRt ATPs will generate safe, unexpected scenarios that adapt to observed learner deficiencies and yet provide fair and comprehensive coverage of all cases/situations. This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-017). 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.

Principal Investigator: Tan Ah Hwee
Affiliation of PI: School of Computing and Information Systems
Funding Source: DSO National Laboratories
Project Synopsis: Computer Generated Forces (CGFs) are artificial entities in tactical simulations modelled after human behaviour. In a previous project with DSO, Intelligent Computer Generated Forces (iCGF) with adaptive capabilities have been developed to provide a viable alternative to script-based CGF. Through reinforcement learning, these iCGFs are designed to acquire knowledge while interacting and operating in a dynamic environment. This project with DSO builds on the capabilities developed in the previous project to enhance the study of iCGF by considering the following two key research questions; 1. How to scale up the applicability of iCGF, in terms of the number of iCGF entities as well as their operability in more complex scenarios and; 2. How to interpret and demonstrate the knowledge learned by iCGF in the form of high-level description.

Co-Principal Investigator: An Jisun
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: The Volkswagen Foundation’s Corona Crisis and Beyond – Perspectives for Science, Scholarship and Society
Project Synopsis: What determines the coverage of Covid-19 and related political contestation in traditional and new media? In collaboration with the University of Bamberg in Germany, this project sets out to identify hidden power structures between media organizations in contemporary hybrid media systems in Germany, UK, USA and South Korea. This will provide insights about the determinants of information quality and the spread of misinformation during a large social crisis in media coverage.

Principal Investigator: Alan Megargel
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore's 100 Experiments Programme
Project Synopsis: This project addresses the inefficiencies in the lead generation, prospecting, engagement and qualification processes which are important stages in the acquisition of high net worth and ultrahigh net worth clients in a financial services business. The current process of digital leads generation and prequalifying is inefficient as it involves the manual qualification of large volumes of names collected from social media posts and marketing campaigns. Furthermore, client advisors do not have enough actionable insights on the leads provided by marketing and prefer to follow their own warm leads (e.g. networking) instead of the ones assigned to them. The objective of this project is to significantly improve the efficiency and effectiveness of these activities by adopting modern digital technologies including Artificial Intelligence models and Big Data analytics. This research project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No. AISG-100E-2020-062).

Co-Principal Investigator: Jiang Lingxiao
Affiliation of co-PI: School of Computing and Information Systems
Funding Source: The Royal Society - International Exchanges 2019 Round 3
Project Synopsis: Current code comparison technology mostly only work in a single language, failing to support inter-language migration. This project looks at closing this gap, through exploring new techniques for comparing code similarity across different languages and paradigms. Through this research, the team aims to improve the quality of software and reduce software development cost.

Principal Investigator: Alan Megargel
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore's 100 Experiments Programme
Project Synopsis: This project addresses the inefficiencies in the KYC (Know Your Customer) and Due Diligence processes which are critical yet resource-intensive activities in a financial services business. These activities are particularly complex in the context of Wealth Management, as compared to retail banking, where the client profiles of high net worth and ultrahigh net worth individuals are typically associated with a wider multi-national network of other family and business relationships and company structures and entities which need to be considered holistically in order to build a full understanding of their profile. The objective of this project is to significantly improve the efficiency and effectiveness of these activities by adopting modern digital technologies including Artificial Intelligence models and Big Data analytics. This research project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No. AISG-100E-2020-058).

Principal Investigator: Lau Hoong Chuin
Affiliation of PI: School of Computing and Information Systems
Funding Source: Fujitsu Laboratories Ltd
Project Synopsis: Under the Fujitsu-SMU Urban Computing and Engineering (UNiCEN) Corp Lab, SMU has undertaken the Digital Platform Experimentation (DigiPlex) project with Fujitsu. The project was carried out using the Digital Annealer (DA), a quantum inspired-technology inspired by Fujitsu. Through the DigiPlex project, certain challenges in solving constrained optimization problems using such technology, and promising methods on tuning of the underlying model parameters to improve run time performance, have been identified. This project aims at developing hyper parameter tuning methodology, machine learning techniques, operations research algorithms, and software tools to enhance quantum-inspired techniques for solving large scale real-world combinatorial optimization problems.

Principal Investigator: Sun Qianru
Affiliation of PI: School of Computing and Information Systems
Funding Source: Agency for Science, Technology and Research
Project Synopsis: This project aims to develop Fast-Adapted Neural Networks (FANNs) and provide specific solutions to equipping the Advanced Manufacturing and Engineering (AME) systems with FANNs. It considers a wide range of AME application examples such as visual inspection of new product parts and automated identification of product defects. It will improve the yield rate and reduce manufacturing costs, when FANNs-based devices are widely installed in the design, layout, fabrication, assembly, and testing processes of production lines. This research is supported by A*STAR under its AME YIRG Grant (Project No. A20E6c0101).

Principal Investigator: Lim Ee Peng
Affiliation of PI: School of Computing and Information Systems
Funding Source: Coleridge Initiative Inc
Project Synopsis: This collaboration with Coleridge Initiative aims to develop entity linking models that automatically identify datasets used in research publications to perform context-rich search and recommendation on research repositories.

Principal Investigator: David Lo
Affiliation of PI: School of Computing and Information Systems
Funding Source: National Satellite of Excellence - Trustworthy Software Systems
Project Synopsis: Smart systems are increasingly dependent on machine learning frameworks for their feature implementation. These frameworks are built on top of many third-party libraries, which depend on many others. Simply trusting and reusing a framework poses a security risk as the framework and third-party libraries it depends on can contain exploitable vulnerabilities. To mitigate this risk, this project will create an advanced solution that identifies vulnerabilities in popular machine learning frameworks.

Principal Investigator: David Lo
Affiliation of PI: School of Computing and Information Systems
Funding Source: Singapore Data Science Consortium
Project Synopsis: This research collaboration with Zhejiang University seeks to unlock the power of large software data stored in open software repositories for automating three common software development tasks: coding (code completion), commenting (code summarization), and identification of software defects (defect prediction).

Principal Investigator: Lim Ee Peng
Affiliation of PI: School of Computing and Information Systems
Funding Source: -
Project Synopsis: Singlish is an English Creole language used in Singapore and it is evolving. New words have been introduced from time to time making it difficult to track the development of this language. With Singlish being used largely in informal conversations, it is extremely challenging for any linguistic expert to keep track of its changes. It is therefore necessary to combine machine learning and human expertise in a crowdsourcing approach to construct and maintain a Singlish dictionary at speed and scale, with reasonable quality. This project will use a combination of machine learning and human efforts in a crowdsourcing approach to learn Singlish words so as to build some sort of Singlish dictionary.

Principal Investigator: Sun Jun
Affiliation of PI: School of Computing and Information Systems
Funding Source: Huawei International Pte Ltd
Project Synopsis: Software developers often make use of a method called satisfiability modulo theories (SMT) for program testing, analysis and verification. Unfortunately, SMT has its limitations as it is ineffective for large software that are usually more complicated. As such, the collaborative project with Huawei, aims to develop new constraint solving techniques based on optimization to improve the effectiveness and efficiency of testing and formal verification of software, so that it can be used on large software as well.

Principal Investigator: Tan Ah Hwee (Project-level)
Affiliation of PI: School of Computing and Information Systems
Funding Source: Agency for Science, Technology and Research's Advanced Material and Engineering Programmatic Fund
Project Synopsis: The K-EMERGE research programme, funded by the RIE2020 Advanced Manufacturing and Engineering Programmatic Grant, proposes a knowledge-based AI approach, complemented by advances in deep-learning NLP methods, to address the need for AI systems that are able to perform deep inference for expert-level diagnosis, explanation, instruction, and decision aiding in the context of complex physical systems. As part of the K-EMERGE programme, this project undertaken by SMU aims to develop computational models and technologies for representation, modelling and learning of domain knowledge extracted from text-based technical documents.

Principal Investigator: Zheng Baihua
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: Through usage of city-scale commuting data (e.g., public transport records captured by smart card EZLink) to perform multimodal data analysis, this project seeks to answer following questions: Question 1: when and where do people commute (related to trip prediction) Question 2: how they commute (related to recovery of the exact routes taken by commuters inside the MRT network) Question 3: why they commute (related to inference of trip purposes)

Principal Investigator: Li Yuchen
Affiliation of PI: School of Computing and Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Synopsis: A novel parallel framework named CONQUEROR is proposed in this project to support large scale concurrent graph query processing. The framework is built upon the popular heterogeneous architecture, which consists of both CPUs (central processing units) and GPUs (graphics processing units), and they aim to develop a set of novel parallel approaches to fully harness the unique characteristics of the heterogenous platform for processing millions of graph queries concurrently.