External Research Grants

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.

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.

This project aims to optimise response of fire engines and ambulances to medical and fire incidents in a prioritised manner.

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.

Project Title: National Satellite of Excellence in Mobile Systems Security and Cloud Security 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

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.

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.

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.

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.

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.