External Research Grants

Principal Investigator: Gao Debin (SMU Co-Lead)
Affiliation of PI: Secure Mobile Centre
Funding Source: Cyber Security Agency of Singapore and National Research Foundation, Singapore
Project Title: Development of Secured Components & Systems in Emerging Technologies through Hardware & Software Evaluation
Project Synopsis: In the past decade, mobile devices and Internet of Things (IoT) have become prevalent in our daily lives, both in business and social settings. The applications of such devices have also been growing exponentially due to the development of technologies that support artificial intelligence (AI) and cloud computing. The advances of cloud and mobile computing in the past decade have fundamentally reshaped the computing infrastructure used by individual, business, and government users into a distributed, heterogenous and collaborative system. Modern applications are hence often built as a fusion of data, software, and services from a mixture of stakeholders. Growing with this evolution are the deep-rooted security concerns over a broad spectrum of issues such as leakage of private data, infringement of software copyrights, and corruption of computation results.

This research programme gathers a core team of experts from the Nanyang Technological University (NTU) and the Singapore Management University (SMU) to tackle these security concerns. The SMU team aims to holistically address security challenges in the mobile and cloud computing ecosystem pivoting on the new so-called "confidential computing" techniques featuring hardware-based Trusted Execution Environments (TEEs). The research outcomes are expected to build up the infrastructure and capabilities towards a zero-trust computing domain for industry and government users.

Principal Investigator: He Shengfeng
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore's AI Governance Research Grant Call
Project Title: AntiGen: Safeguarding Artistic and Personal Visual Data from Generative AI
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).

Principal Investigator: David Lo
Affiliation of PI: Centre for Research on Intelligent Software Engineering
Funding Source: Smart Nation Group's Translational R&D 2.0 Grant (TRANS2.0)
Project Title: Titan Code Analysis: Vulnerability Discovery with Large Code Models
Project Synopsis: This project involves research and engineering effort to build an intelligent vulnerability discovery system, and to secure citizen data by addressing vulnerabilities in Government software and Digital Government services.

Principal Investigator: Zhu Feida
Affiliation of PI: School of Computing and Information Systems
Funding Source: Zeasn Technology Pte Ltd
Project Title: Tokenized Economy and Collaborative Intelligence for Web 3 Media Industry
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 Title: Data-driven Optimisation and Artificial Intelligence for Future Fintech
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 Title: Sensors In-Home for Elder Wellbeing (SINEW)
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 Title: Web 3 Security
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 Title: Acute workforce response to “Demand pulled” patient lifecycle data via Generative Flow Networks and Graph Neural Networks
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).

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 Title: ProExpan: Proactive Ontology Expansion for Conversational Agents
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 Title: Towards Building Unified Autonomous Vehicle Scene Representation for Physical AV Adversarial Attacks and Visual Robustness Enhancement (Stage 1a)
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: Xie Xiaofei (SMU PI)
Affiliation of PI: Centre for Research on Intelligent Software Engineering (RISE)
Funding Source: Cyber Security Agency of Singapore
Project Title: Trustworthy AI Centre NTU (TAICeN)
Project Synopsis: The TAICeN project is focused on developing AI-based solutions for cybersecurity tasks, with two primary research areas: AI for Cybersecurity and Trustworthy AI. Part 1, AI for Cybersecurity, investigates advanced AI technologies for defending against cyber threats such as malware detection, intrusion detection on government cloud systems, crypto attribution, and insider attack attribution. Part 2, Trustworthy AI, ensures the security, robustness, and explainability of the AI-based solutions developed in Part 1. Part 1 will be primarily conducted by NTU, NUS, and BGU, in collaboration with local government agencies. Part 2, led by SMU, explores three key research topics: AI Security, Robustness, and Explanation. AI Security focuses on techniques to mitigate inference attacks, model extraction attacks, adversarial attacks, and poisoning attacks on the solutions. AI Robustness aims to provide quality assurance for AI systems by offering methods to evaluate, debug, and improve AI systems. AI Explainability enables human comprehension and reasoning of the AI decision-making process, which is crucial when predictions have national or safety-critical implications. By addressing both research challenges, the TAICeN project aims to develop effective and trustworthy AI-based solutions for cybersecurity that can keep pace with cybercriminals, automate threat detection, and defend against attacks. Specifically, Prof. Xie Xiaofei and Prof. Sun Jun will focus on the AI Robustness and AI Explainability work packages together with NTU and the relevant partner institutions to demonstrate their research results together with the relevant translation partners.

Principal Investigator: David Lo
Affiliation of PI: School of Computing and Information Systems
Funding Source: National Research Foundation
Project Title: TrustedSEERs: Trusted Intelligent Work Bots for Engineering Better Software Faster
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 Title: Unleashing the Power of Pre-trained Models for VisualQA: A Skill-based Framework
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 Title: Mobile-friendly Data Visualization
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 Title: Food Recognition: Causality-driven Cross-modal Cross-lingual Domain Adaptation
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 Title: Executable AI Semantics for AI Framework Analysis
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 Title: Text Style Transfer with Pre-Trained Language Models
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 Title: Weakly-supervised Semantic Segmentation and Its Applications in SAR Images
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 PI: School of Computing and Information Systems
Funding Source: Info-communications Media Development Authority of Singapore
Project Title: TradeMaster: Reinforcement Learning-based Quantitative Trading Toolkit
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 Title: Optimizing Supply Chain Resilience with Quantum Sampling
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 Title: Next generation roster management via reinforcement learning
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).

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 Title: Universal Pre-training of Graph Neural Networks
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 Title: Digital Wellbeing: Identifying, Testing and Measuring Framework Indicators Towards Digital Readiness, Inclusion and Safety
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: David Lo
Affiliation of PI: Research Lab for Intelligent Software Engineering / School of Computing and Information Systems
Funding Source: National Cybersecurity Research and Development Programme at the Cyber Security Agency of Singapore
Project Title: Less is More: Addressing Mobile Application Security and Privacy through Debloating
Project Synopsis: This project will develop a new approach, named MINIMA, for protecting end users against privacy and security risks in mobile operating systems. The success of this project can lead to an effective strategy to allow citizens to better protect themselves against cyber threats by minimising their individual attack surface.

Principal Investigator: Lauw Hady Wirawan
Affiliation of PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Title: Lifelong Learning for Recommender Systems: Continual, Cross- Domain, and Cross-Platform Approaches
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 Title: Slide++: Automatic Augmentation of Academic Slides Towards AI-Enabled Student-Centred Learning
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 Title: AP-Coach: AI-based formative feedback generation to improve student learning outcomes in introductory programming courses
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-Investigator: Griffin Paul Robert
Affiliation of Co-I: School of Computing and Information Systems
Funding Source: Quantum Engineering Programme
Project Title: Quantum-Enhanced Modelling of Financial Time-Series Data for Rare Event Forecasting
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 Title: On Demand Delivery Assignment Recommender
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 Title: PERFLEXO: a PERsonalized, FLExible, and controlled Output-size framework for multi-objective preference queries in large databases
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 Title: Task-Specific Data Augmentation in Class-Incremental Learning Systems
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 Title: Enhancing Digital Annealer (EDA)
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: Sun Jun
Affiliation of PI: Research Lab for Intelligent Software Engineering / School of Computing and Information Systems
Funding Source: Huawei International Pte Ltd
Project Title: On the Runtime Verification of Trustworthy Deep Learning Systems
Project Synopsis: This project aims to develop a practical method for certifying real-world AI-based systems based on a novel combination of static and dynamic verification, targeting systems with a certification requirement similar to that of EAL 6-7 for traditional software systems. We accomplish this by developing a completely new set of algorithms, which are designed to battle the scalability limitation of static verification techniques and connect static and dynamic verification, and use the partial verification engine developed to solve the verification problem systematically.

Principal Investigator: Robert Deng
Affiliation of PI: School of Computing and Information Systems
Funding Source: Huawei International Pte Ltd
Project Title: Attribute-based Authentication and Authorization Technologies
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 Title: ADrone: Auditing Drone Behaviours for Accountability of Criminal/Malicious Activities
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 Title: Improving Fairness and Accessibility of Crowd Work
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:

1. Present information to encourage crowdsourcing requesters pay a fairer wage to online workers; and

2. 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 Title: Supply Chain Risk Resiliency Project for Supply Assurance/Procurement and Logistics
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: Lo Siaw Ling
Affiliation of PI: School of Computing and Information Systems / Living Analytics Research Centre
Funding Source: ST Engineering Mission Software & Services Pte Ltd
Project Title: Actionable Situational Intelligence for Urban Events using Social Media
Project Synopsis: With its open and broadcasting nature, social media is often the platform to go to when an incident occur. This study concentrates on urban events, which can be an incident of social disorder or a crisis such as a sudden riot in a city. While prior studies mainly focus on detecting crisis events, this new study goes beyond detection to focus on actionable intelligence and proposes an in-depth analysis of the event including timeline-based situational and emotional changes. The objectives of this study are, 1) develop an approach to extract and analyze actionable situational intelligence from social media, and 2) research on new/novel approaches to summarize key information for interpretation of the results, e.g., relationship of key entities.

Principal Investigator: Cheng Shih-Fen
Affiliation of PI: School of Computing and Information Systems
Funding Source: Mercurics Pte Ltd
Project Title: Smart Barrier-Free Access (SMARTBFA) v2
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 Title: Learning by Doing in the Age of Big Data
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 Title: The Science of Certified AI Systems
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 Title: CUDO Customization and Integration
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).

Co-Principal Investigator: Fang Yuan
Affiliation of Co-PI: School of Computing and Information Systems
Funding Source: Agency for Science, Technology and Research (A*STAR) AME Programmatic Funds
Project Title: Learning with Less Data
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 Investigators: Pradeep Varakantham, Akshat Kumar
Affiliation of co-PIs: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Title: The “Other Me”: Human-Centered AI Assistance In Situ
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 co-PI: School of Computing and Information Systems
Funding Source: AI Singapore Research Programme
Project Title: TrustFUL: Trustworthy Federated Ubiquitous Learning
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 Title: Trust to Train and Train to Trust: Agent Training Programs for Safety-Critical Environments
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 Title: Scalable and Explainable Intelligent Computer Generated Forces (iCGF)
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 PI: School of Computing and Information Systems
Funding Source: The Volkswagen Foundation’s Corona Crisis and Beyond – Perspectives for Science, Scholarship and Society
Project Title: Discursive power in the coverage of Covid-19: An international comparison of hidden structure in contemporary media systems identified with deep learning techniques in text, images, and video
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 Title: Autonomous Prospecting and Product Recommendations
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 PI: School of Computing and Information Systems
Funding Source: The Royal Society - International Exchanges 2019 Round 3
Project Title: Making Software Development Language-Agnostic Through Cross-Language Mapping and Migration
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 Title: Autonomous Onboarding and Periodic KYC review (PKR)
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 Title: Enhancing Digital Annealer (EDA)
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 Information Systems
Funding Source: Agency for Science, Technology and Research
Project Title: Fast-Adapted Neural Networks for Advanced AI Systems
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 Information Systems
Funding Source: Coleridge Initiative Inc
Project Title: Rich Context - Automated Data Inventory
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 Information Systems
Funding Source: National Satellite of Excellence - Trustworthy Software Systems
Project Title: Uncovering Vulnerabilities in Machine Learning Frameworks via Software Composition Analysis and Directed Grammar-Based Fuzzing
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 Information Systems
Funding Source: Singapore Data Science Consortium
Project Title: Making Big Code Active: From Billions of Code Tokens to Automation
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 Information Systems
Project Title: Singlish Learning by Crowdsourcing
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 Information Systems
Funding Source: Huawei International Pte Ltd
Project Title: Constraint Solving based on Optimisation for Software Analysis
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 Information Systems
Funding Source: Agency for Science, Technology and Research's Advanced Material and Engineering Programmatic Fund
Project Title: K-EMERGE: Knowledge Extraction, Modelling, and Explainable Reasoning for General Expertise
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 Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Title: BeyondTravel: a Multimodal Travel Records Analytics Framework
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 Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Title: CONQUEROR:CONcurrent graph QUERy processing on CPU-GPU heterOgenous aRchitecture
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.

Principal Investigator: Paul Griffin
Affiliation of PI: School of Information Systems
Funding Source: OneConnect Financial Technology (Singapore) Co. Pte. Ltd.
Project Title: Distributed Ledger Research
Project Synopsis: This research aims to investigate the characteristics of quantum computing for distributed ledger technologies, which include Distributed Ledgers. Distributed Ledgers are now live and in use and there are many issues and limitations such as in scalability, security and determinism. Areas under consideration for improvement is the consensus mechanism and interoperability between Distributed Ledgers and the aim of this research is to investigate quantum mechanisms for these areas.

Principal Investigator: Sun Jun
Affiliation of PI: School of Information Systems
Funding Source: National Research Foundation (NRF) - Agence Nationale de la Recherche (ANR) Joint Grant Call
Project Title: Provable Mitigation of Side Channel through Parametric Verification
Project Synopsis: The Spectre vulnerability has recently been reported to affect most modern processors. Attackers can extract information about the private data using a timing attack. This is an example of side channel attacks, where secure information flow through side channels unintentionally. How to systematically mitigate such attacks is an important and challenging research problem. This project proposes to automatically synthesise mitigation of side channel attacks using well-developed verification techniques. Given a system with design parameters which can be tuned to mitigate side channel attacks, this approach will automatically generate provable “secure” valuations of the parameters.

Principal Investigator: Paul Griffin
Affiliation of PI: School of Information Systems
Funding Source: Monetary Authority of Singapore's Financial Sector Development Fund - Artificial Intelligence & Data Analytics Grant (Research Track)
Project Title: Exploring the advantage of a quantum system for machine learning applied to credit scoring
Project Synopsis: The objective of the project is to build a predictive machine learning model implemented on a quantum computer and a simulated quantum computer which has the potential to improve credit scoring accuracy. Credit scoring provides lenders and counterparties better transparency of the credit risk they are taking when dealing with a counterparty. Machine learning approaches allow for automated credit scoring feasible for a broad coverage of small companies. The current approaches rely on classical machine learning algorithms applied to broad datasets that combine company, accounting, and socio-economic information. Improving the learning algorithms is thus an important element to providing credit risk transparency.

This project will explore the use of quantum algorithms which cannot be implemented on classical machines today. This may open the route to a practical quantum supremacy for a financial application and create business advantages for the financial industry as quantum computing continues to improve.

Principal Investigator: Cheng Shih-Fen
Affiliation of PI: School of Information Systems
Funding Source: Changi Airport Group Pte Ltd
Project Title: Intelligent Taxi Queue Management
Project Synopsis: The objective of this project is to provide new methods and systems for taxi queue management to better manage and predict taxi demand and supply at the Changi Airport. Models and systems that could more accurately predict the current and future taxi demand and drivers’ waiting times at different terminals would be developed, to predict taxi supply, passenger demand and driver waiting times at all taxi queues. Through deriving segmented and personalised models on how drivers react to incentives and related information, the project will also study how to best balance the supply and demand at all terminals in a workable & cost effective way. The team will determine whether there is a shortage of taxi supply either now or in the near term, and if so, construct real-time/pre-emptive plans to attract the right number of drivers based on individual driver’s reaction & behaviour model.

In addition, this project should provide useful and timely information to passengers (via different means such as digital display, mobile app alerts, SMS alerts, etc.) when there are long queues at taxi stands that cannot be resolved within the next 10 to 15 minutes. The system should provide alternative and available transport options nearby (i.e. MRT, private-hire cars, limousine, shuttle bus, etc.) with related way finding, costing and traveling time information.Mobile devices and mobile applications are increasingly important for people's daily life, and their security and privacy are raising more and more concerns. "Do you allow the app to access your contacts, photos, media, files, messages, …" becomes a common question faced by users when they start to use an app, but such permission control mechanisms put too much burden on users. Most users may not understand well the purposes of the accesses and the implications of granting permissions, and simply grant the permissions most of the time, leading to significant misuses of their privacy-sensitive data by apps. This proposal aims to build up the capabilities that enable automated, finer-grained, and customisable permission controls, which will promote a privacy ecosystem that keeps users aware, while reducing the burden on users, and pushes app developers to improve the privacy protection grades of their apps.

Principal Investigator: Jiang Lingxiao
Affiliation of PI: School of Information Systems
Funding Source: National Satellite of Excellence - Mobile Systems Security and Cloud Security
Project Title: AutoPrivacyModel: Automated Feature Modelling for Identifying Illegitimate Uses of Privacy-Sensitive Data in Mobile Applications
Project Synopsis: Mobile devices and mobile applications are increasingly important for people's daily life, and their security and privacy are raising more and more concerns. "Do you allow the app to access your contacts, photos, media, files, messages, …" becomes a common question faced by users when they start to use an app, but such permission control mechanisms put too much burden on users. Most users may not understand well the purposes of the accesses and the implications of granting permissions, and simply grant the permissions most of the time, leading to significant misuses of their privacy-sensitive data by apps. This proposal aims to build up the capabilities that enable automated, finer-grained, and customisable permission controls, which will promote a privacy ecosystem that keeps users aware, while reducing the burden on users, and pushes app developers to improve the privacy protection grades of their apps.

Principal Investigator: Gao Debin
Affiliation of PI: School of Information Systems / Secure Mobile Centre
Funding Source: National Satellite of Excellence - Mobile Systems Security and Cloud Security
Project Title: Fine-grained Dynamic Analysis and Scalable Static Analysis for Android Applications
Project Synopsis: Android has become the most popular operating system for mobile devices, with millions of applications published for users to download and use. However, some of these applications may harbour security gaps that render them vulnerable to determined attacks from the Internet, or could have been intentionally constructed with malicious intent. This project aims to develop three novel and complementing technologies for the security analysis of Android applications, under different use case scenarios and infrastructure constraints.

Principal Investigator: Ding Xuhua
Affiliation of PI: School of Information Systems / Secure Mobile Centre
Funding Source: National Satellite of Excellence - Mobile Systems Security and Cloud Security
Project Title: A system framework for reliable and dependable incident response on mobile devices
Project Synopsis: The research aims to address security challenges arising from the usage of security-sensitive applications without trusting the phone’s operating system, which is known to be vulnerable to attacks due to its enormous code size and large attack surface.

Principal Investigator: Sun Jun
Affiliation of PI: School of Information Systems
Funding Source: National Satellite of Excellence - Trustworthy Software Systems
Project Title: SpecTest: Specification-based Compiler Fuzzing
Project Synopsis: Compilers are a key technology of software development. They are relevant for not only general purpose programming languages (like C/Java) but also many domain specific languages. Compilers are error-prone, especially concerning less-used language features. Existing compiler testing techniques often rely on weak test oracles which prevents them from finding deep semantic errors. The project aims to develop a novel specification-based fuzzing method named SpecTest for compilers. SpecTest has three components: an executable specification of the language, a fuzzing engine which generates test cases for programs in the language, and a code mutator which generates new programs for testing the compiler. SpecTest identifies compiler bugs by comparing the abstract execution of the specification and concrete execution of compiled program. Furthermore, with the mutator, SpecTest can systematically test those less-used language features.

Principal Investigator: Ding Xuhua
Affiliation of PI: School of Information Systems
Funding Source: National Satellite of Excellence - Trustworthy Software Systems
Project Title: A Novel Hybrid Kernel Symbolic Execution Framework For Malware Analysis
Project Synopsis: Today’s malware analysis tools, especially those on kernel attacks, face the barrier of insufficient code path coverage to fully expose malicious behaviours, as that requires systematic exploration of kernel states. Although symbolic execution is the well-established solution for benign programs’ code coverage, it does not overcome that barrier because of its susceptibility to attacks from the running target under analysis and incapability of managing complex kernel execution. This project aims to innovate cutting-edge techniques to automatically and systematically generate code paths for maliciously-influenced kernel behaviours.

Principal Investigator: Gao Debin
Affiliation of PI: School of Information Systems
Funding Source: National Satellite of Excellence - Trustworthy Software Systems
Project Title: Enhanced function signature recovery for control-flow integrity enforcement on compiler optimized executables
Project Synopsis: Control-Flow Integrity (CFI) enforcement is a promising technique in producing trustworthy software. This project focuses on function signature recovery, which is a critical step in CFI enforcement when source code is not available. Current approaches rely on the assumption of matching function signatures at caller and callee sites in an executable; however, various compiler optimisations violate well-known calling conventions and result in unmatched function signatures recovered. The project aims to design and implement an automatic system to produce CFI-enforced program executables.

Principal Investigator: Sun Jun
Affiliation of PI: School of Information Systems
Funding Source: National Research Foundation, under the AI Singapore Research Programme
Project Title: Explaining AI with the Right Level of Abstraction
Project Synopsis: Artificial Intelligence (AI) technologies have been under rapid development thanks to machine learning based on deep neural networks and their applications. Despite the exceptional performance of deep neural networks, these complex models are often beyond human understanding and thus work in a black-box manner. The research aims to address the problem of explaining AI for AI system designers and expert AI system users who are required to know how AI makes decisions.

Principal Investigator: Robert Deng
Affiliation of PI: School of Information Systems
Funding Source: Huawei International Pte Ltd
Project Title: Zero Touch Identity Management for IoT devices: Using Attribute Based Encryption for Identity Information Access Control
Project Synopsis: This project aims to provide secure remote access control over identity information of Internet-of-Things (IoT) devices to prevent sensitive information from being stolen.

Principal Investigator: David Lo
Affiliation of PI: School of Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Title: DeepSense: Deep Media Sensing for Software API Recommendation
Project Synopsis: Software development today relies on Application Programming Interfaces (APIs), and identifying suitable APIs to use can directly influence the success or failure of a software development project. While a large number of third-party APIs are available on the internet, selecting suitable APIs for a project can be challenging. This research proposes a big-data, deep-learning, and exploratory-search approach for API recommendation called DeepSense to improve software developers’ productivity, and the success of this project will benefit the software engineering and artificial intelligence research community, software developers, and institutions developing IT solutions.

Principal Investigator: Pradeep Reddy Varakantham
Affiliation of PI: School of Information Systems
Funding Source: Ministry of Home Affairs
Project Title: MHA-Merlion Initiative – SCDF 02
Project Synopsis: This project aims to optimise response of fire engines and ambulances to medical and fire incidents in a prioritised manner.

Principal Investigator: Rajesh Balan
Affiliation of PI: School of Information Systems
Funding Source: Microsoft Research Asia
Project Title: Identifying Personas Using Video Analytics
Project Synopsis: Awarded under the Microsoft Research Asia Collaborative Research Program 2019, this project aims to extend existing video analytics solutions to be able to process videos of people entering a building and automatically classify them into different categories, in real-time and with as minimal prior knowledge of the people as possible.

Principal Investigator: Robert Deng
Affiliation of PI: School of Information Systems
Funding Source: National Research Foundation's National Cybersecurity R&D Programme
Project Title: National Satellite of Excellence in Mobile Systems Security and Cloud Security
Project Synopsis: Supported by NRF, the National Satellite of Excellence in Mobile Systems Security & Cloud Security aims to develop a technology pipeline that would address the mobile system security and mobile cloud security needs for real-time monitoring/decision systems used in critical smart nation applications. It will focus on research in the following core competencies:

• Privacy-preserving access and search of encrypted data
• Privacy-preserving computation over encrypted data
• Applications of privacy-preserving technologies in in-home elderly monitoring systems

Principal Investigator: Archan Misra
Affiliation of PI: School of Information Systems
Funding Source: National Research Foundation's NRF Investigatorship
Project Title: C2SEA: Coordinated Cyber-physical Sensing & Edge Analytics
Project Synopsis: This project will pioneer new capabilities in real-time, ultra-low power, pervasive sensing (e.g., tracking a user’s pointing gestures with cm-level accuracy), by building technologies that enable a collection of resource-constrained wearable and cheap IoT devices to collaboratively execute complex machine intelligence tasks. The research will advance Singapore’s capabilities in areas such as smart manufacturing and smart cities.

Principal Investigator: Tan Hwee Pink
Affiliation of PI: School of Information Systems
Funding Source: NTUC Health Co-operative Ltd
Project Title: Smart-Tech Attendance and Home Visits Recording System
Project Synopsis: SIS and NTUC Health Co-operative Limited (NHCL) are collaborating to implement and pilot a smart technology system to enhance NHCL’s operational efficiency and productivity. With improved productivity, staff can be availed to perform value-added tasks. SMU will play an active role in analysing the captured data, as well as providing actionable insights to enable NHCL to focus and improve on the delivery of care to their clients.

Principal Investigator: Benjamin Gan
Affiliation of PI: School of Information Systems / DHL-SMU Analytics Lab
Funding Source: DHL
Project Title: DHL-SMU Analytics Lab
Project Synopsis: The DHL-SMU Analytics Lab was first established in September 2016 and it has been extended for another two years with fresh investment by DHL for further collaboration. The Lab is a joint initiative by SMU and DHL, aimed at driving innovation and development of applicable advanced analytics concepts across the supply chains globally.

Principal Investigator: Fang Yuan
Affiliation of PI: School of Information Systems
Funding Source: AI Singapore’s AISG Research Programme
Project Title: One-shot learning: A crucial learning paradigm towards human-like learning
Project Synopsis: This project explores new approaches to investigating the fundamental research problem of learning from small (labelled) data, called one-shot learning or few-shot learning, and will develop new algorithms and techniques to devise one-shot learning machines with human-like learning capabilities.

Principal Investigator: Akshat Kumar
Affiliation of PI: School of Information Systems
Funding Source: Ministry of Education’s Academic Research Fund Tier 2
Project Title: Data driven collective decision making for urban system optimization
Project Synopsis: This project tackles the problem of developing intelligent multi-agent planning and decision making algorithms, which can scale to a much larger number of agents and support significantly more complex agent behaviour, than currently possible. The proposed work will propose new models and algorithms that are applicable to a wide range of problems of practical importance, particularly in urban system optimisation.

Principal Investigator: Pradeep Reddy Varakantham
Affiliation of PI: School of Information Systems
Funding Source: Ministry of Home Affairs
Project Title: MHA-Merlion Initiative – SPF 02
Project Synopsis: This project aims to use game theoretic models to randomise patrols and visits conducted by the police, with the goal to minimise their predictability and enhance security efforts.

Principal Investigator: Tan Hwee Pink
Affiliation of PI: School of Information Systems
Funding Source: Ospicon Systems
Project Title: Remote, non-invasive fiber-optic based monitoring of infants and toddlers
Project Synopsis: The project is a collaboration between the School of Information Systems and Ospicon Systems, a pioneer of the world’s first patented optical fiber based breath-sensing technology for infants and the elderly. By leveraging on the School's and Ospicon Systems’ respective strengths in the areas of IoT data analytics and fiber-optic sensing, the project team seeks to further enhance the performance of Ospicon’s product offerings.

Principal Investigator: Jiang Jing
Affiliation of PI: School of Information Systems
Funding Source: DSO National Laboratories
Project Title: Characterizing and identifying misinformation from the web
Project Synopsis: The project seeks to identify misinformation on the web by studying the content and propagation patterns of known cases of misinformation. Through the analysis of these misinformation, the research team seeks to propose methods that will allow for automatic flagging of suspicious pieces of information circulating on the web which can be sent to experts for verification.

Principal Investigator: Gao Debin
Affiliation of PI: School of Information Systems
Funding Source: AI Singapore’s 100 Experiments
Project Title: Intelligent and non-intrusive monitoring of Android devices for protection against data-infringing malware
Project Synopsis: The project is a joint collaboration with Acronis Asia Research and Development Pte Ltd, and it aims to develop a non-intrusive monitor that leverages artificial intelligence to achieve protection against data-infringing malware on Android devices. A dynamic analysis framework would be built to perform dynamic analysis of Android apps on un-rooted Android phones to achieve the feature of non-intrusiveness, while a deep learning solution would be developed to identify the specific Android app responsible for performing sensitive operations.

Principal Investigator: Archan Misra
Affiliation of PI: School of Information Systems
Funding Source: US Army Research, Development and Engineering Command International Technology Center-Pacific
Project Title: Socio-physical sensing & analytics for urban anomaly detection
Project Synopsis: Awarded with a second year funding, the project looks into building fundamental data fusion techniques to combine data from both physical urban sensors and social media sensing to generate improved insights into the evolution of urban events, and a software library of tools that extract and combine analysis techniques across multiple socio-physical sensing channels.

Co-Principal Investigators: Tan Hwee Pink, Tan Hwee Xian
Affiliation of PI: School of Information Systems
Funding Source: Tote Board’s Tote Board-Enabling Lives Initiative
Project Title: Smart accessibility & mobility for barrier-free access
Project Synopsis: The project, led by Trampolene Limited, aims to design a scalable, self-sustaining system that can collect, classify and determine accessible point-to-point routes that are suitable for barrier-free access.

Principal Investigator: Lim Ee-Peng
Affiliation of PI: School of Information Systems
Funding Source: Public Transport Council
Project Title: Sentiment analysis of MRT’s event-related social media content
Project Synopsis: The project aims to understand MRT events and commuting experience through sentiment analysis of public tweets related to MRT events generated by Singapore's Twitter users.