School of Computing and Information Systems

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Research Awards and Achievements

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Research Focus Areas

As a computing school in Singapore Management University, our research is uniquely defined by the twin emphasis of Academic Scholarship in Computing Technology and Practice Scholarship in Solution Development.

Our Academic Scholarships are directed to 3 selected areas, mainly Artificial Intelligence and Data Science, Human-Machine Collaborative Systems, and Information Systems and Technology.

At the same time, we need to go beyond technology creation in order to achieve practical impact.

We emphasise solution development which entails integrating different technologies, integrating with business models and processes, addressing usability, deployment and product management concerns, aligning with local ethical norms as well as legal and regulatory frameworks and ultimately, fulfilling user and organisational needs.

That is what we mean by Practice Scholarship.

The application areas that we direct our Practice Scholarship to are: Education Pedagogy, Urban Systems and Operations, Active Citizenry and Communities, and Safety and Security.

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Centres, Labs & Initiatives

A*STAR-SMU Joint Lab in Social and Human-Centered Computing

To conduct research which integrates social sciences and humanities disciplines with advances in computational intelligence and digital technologies.

Centre on Security, Mobile Applications and Cryptography  (SMC)

Formally known as Secure Mobile Centre, the Centre on Security, Mobile Applications and Cryptography conducts research on the following areas:
  • Systems and mobile security
  • Software and data security
  • Cryptography and cloud security
  • Digital platforms device security
  • Program analysis and blockchain

Centre for Research on Intelligent Software Engineering (RISE)

To conduct both applied and fundamental research in the intersection of software engineering, artificial intelligence, and cybersecurity for improving software quality and reducing costs.

Research Lab for CollaborAtive, Robust and Explainable AI-based Decision Making

To build trustworthy (robust and explainable) AI decision making frameworks and systems that collaborate with humans and/or other AI systems. The initial focus of the lab is on developing frameworks for trustworthy training and situated assistance for non-expert humans in safety critical environments.

Research in Education Programmes