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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
