We seek to change the traditional mindset of researchers – to collaborate across disciplines for impactful research rather than working in silos on “small” problems, and to work with industries and public agencies on challenging rea-life problems rather than problems that are only of academic interests.
The Institute facilitates joint collaborations across the departments, and provides a single focal point for industries and public agencies to tap into the broad spectrum of scientific and technological expertise in the University. We focus on solving real-life data science problems that meet the needs of various sectors, and leads to the potential for technology transfer and translation of research outputs for real world deployments.
The Institute collaborates with external industry to tackle real world data science problems and provides solutions.
Natural language processing for Q&A in indigenous/vernacular languages
The project is a newly-funded international collaboration between Singapore and New Zealand on data science, focusing on multi-lingual Q&A for revitalising and facilitatng indigenous/vernacular languages in Southeast Asia and New Zealand through AI and data science.
AI in Health Grand Challenge
Transforming Chronic Care for Diabetes, Hypertension and Hyperlipidaemia with AI (by NUS Institute of Data Science, NUS School of Computing, SingHealth and its polyclinic network, HSRC, SNEC, NHCS, Duke-NUS)
Toward Trustable Model-centric Sharing for Collaborative Machine Learning
Collaborative machine learning is an appealing paradigm to build high-quality machine learning (ML) models by training on the data from many parties. However, concerns over trust and security have hindered the sharing of data between organizations. Much efforts have been focused on breaking the “data silos”, but the silos are inherently difficult to break at the data level, as personal or proprietary data are often involved.
Click here to view past projects