Our Approach
Overview
Through transdisciplinary and translational research, we aim to address real-life grand challenge problems that call for collaborative effort from multiple disciplines.
We seek to collect data for sharing and to abstract out challenging research questions from real-life problems that can be investigated to gain a deeper understanding of data science. This allows researchers to collaborate for a larger cause, and at the same time, retain their freedom to address challenging and meaningful abstracted problems.
We envision the research activities within the Institute to not only push the boundary of current knowledge on data science through upstream research, but also lead to solutions that can be translated for uses that are impactful and of lasting value (e.g., improve productivity, promote healthy living, improve quality of life, etc).
Translational Research
Practically every sector of our society today has accumulated or generated a large quantity of data that needs to be mined to extract useful information for businesses, biomedical, healthcare, the internet of things, public policy, law, government, media and emerging areas such as smart nations.
The Institute will develop enabling technologies that encompass horizontal domains related to mathematics, statistics and computing. These enabling technologies will be used to support a wide variety of vertical sectors.
Effective solutions for the vertical sectors require discipline-specific expertise in tandem with core computing and mathematical/statistical expertise. NUS is able to draw on core competency in many of these domains from various departments including Saw Swee Hock School of Public Health, School of Medicine, Faculty of Arts and Social Sciences, Risk Management Institute, Global Asia Institute, and Business School.
When we work on real-life problems that meet the needs of the various sectors, there is great potential for technology transfer and translation of research output for real world deployment.
The Institute will promote strong interaction and collaboration with the industry and government agencies whose “businesses” are driven by big data, such as hospitals, telecommunications, finance, and pharmaceutical companies. Regular dialogue and engagement between the NUS community and these stakeholders will identify key strategic directions that are academically challenging and relevant, and this will ensure the translation of the research outcomes into solutions that are impactful and of lasting value.
Data Hub
The research at the Institute naturally operates on a variety and large collection of real-world datasets. Some of these are publicly available data while others may have restrictions imposed on their use by the data owners. In addition, the Institute’s research may further create, generate and collect data. Our position in Asia will allow us to collect unique datasets to tackle problems that are not present or less common in those Western world institutes.
The Institute will serve as a trusted broker and custodian of these datasets for research. Whenever possible, the Institute will share the datasets with NUS researchers and external partners affiliated with the Institute. The Institute will also develop best practices across NUS and the research community to improve data accessibility. The rich datasets accumulated by the Institute will continue to provide for developing transdisciplinary research in data science, and the building up of talents for a data-driven economy.
