Awarded under MOE's Academic Research Fund Tier 3, the funding extends the existing Tier 3 project for one year.
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.
This project evaluates the effectiveness of training employees with Mindfulness/Loving-Kindness at Rakuten Institute of Technology (RIT). SMU (through the Mindfulness Initiative @ SMU) will work with RIT on (1) the design and methodology of the research study; (2) the training interventions used for mindfulness and compassion; (3) and the measurement of all study variables. SMU will also conduct statistical analyses of the study data and provide RIT with anonymised raw data and collaborate in publishing an academic paper(s) of the study findings. The findings will be shared with the scientific community through presentations, academic papers, and practitioner articles.
SMU has been awarded a grant by the Ministry of Law to develop the Singapore International Dispute Resolution Academy (SIDRA) as a research centre demonstrating thought leadership on international dispute resolution. SIDRA used to be a subsidiary of the Singapore Academy of Law and the Singapore Mediation Centre, and with its transition to SMU, it will embark on the projects to further advance research on dispute resolution and mediation.
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.
With the data market growing at an exceptional pace, more sophisticated strategies using machine learning techniques have been introduced to conduct economic forecasting. Awarded by the Monetary Authority of Singapore (MAS), under the MAS Financial Sector Development Fund, this project aims to develop a pedagogical framework for forecasting economic activities, through reviewing and identifying complementarities between the two disciplines – conventional econometric methods and machine learning techniques.
This is a continuation of the collaboration with Chuo University for their research project, which aims to clarify the diversity of legal systems in the Asia-Pacific region, with a focus on three specific areas – civil law (focused on business transaction), dispute resolution, and data privacy.
Through regression analyses of longitudinal and panel data, this project aims to have a better understanding of how the recent wave of relaxing listing requirements, especially the removal of "one share one vote" rule, affect shareholders and other stakeholders on a global scale through investigating the situation in Hong Kong and Singapore.
In recent years, increasing amount of rich and informative data have become available and these big data are helpful in establishing cause and effect relationships in empirical research. However, the use of big data is not without challenges. This project seeks to tackle these new challenges that applied economists encounter with the use of big data, through improving three widely used causal inference methods in modern economics.