Programme Structure & Curriculum

Our PhD students are trained to work across research areas. The curriculum covers three blocks that have high market demands - Artificial Intelligence & Data Science, Human-Machine Collaborative Systems, and Information Systems & Technology.

The PhD programme aims to train students to work at the intersection of IT and business, specifically,

• Research on deep technology challenges in real computing systems that impact society or business.
• Research on tools and methodologies to translate societal challenges or business goals into technology solutions.

Students to work with industry datasets and commercial platforms. Students will learn to conduct their research in the context of real information systems and business goals.

The PhD theses will emphasize solving real-world problems and building usable technology solutions, rather than working only on component technologies. Selected students will also get to participate in industry projects, and experiment with real data sets on commercial test-beds to provide industry relevance to the students' research. This aspect of the PhD training is critical to grooming promising students for good job openings, rather than leaving placement till near the students' graduation.

Our PhD students will acquire professional skills that are important in industrial R&D, such as competitive intelligence & intellectual property management. Students will have opportunities to network with academic researchers and industry practitioners.

To achieve our focus of applied research at the intersection of IT and business, the curriculum provides:

• Depth in the primary area to which a candidate's thesis belongs.
• Breadth in IT and management.
• Professional skills like research methodology, and competitive intelligence & intellectual property management.

3rd and 4th year PhD students will co-teach classes with experienced professors, and also mentor fresh PhD students.

Students will attend courses in Artificial Intelligence & Data Science, Human-Machine Collaborative Systems, and Information Systems & Technology. The breadth requirements are intended to help the PhD students establish their contact networks and to expose them to industry practices.

Students will attend courses in Artificial Intelligence & Data Science, Human-Machine Collaborative Systems, and Information Systems & Technology. The breadth requirements are intended to help the PhD students establish their contact networks and to expose them to industry practices.

• A student begins by doing apprenticeship in a chosen research area with an assigned advisor.
  
  
• The first semester is spent on literature survey and identifying potential thesis topics, and attending breadth courses.
  
  
• By the end of the first semester, the student should choose two areas to focus in, one of which becomes the depth area and the other the breadth area.

• In the second semester, the student attends advanced courses in the depth and breadth areas.
• By the end of the second semester, the student should have delivered survey/research papers and/or grant proposals.
  
  
• The advanced courses and research apprenticeship are graded to ensure that students do not neglect research while doing coursework in the first year of candidature.
  
  
• The PhD programme committee will evaluate the student's readiness to enter into the next phase based on his/her course grades, as well as papers from the Research Apprenticeship.

The student is expected to form a 4-member dissertation committee, and pass an oral qualifying examination followed by a dissertation proposal within the second year of candidature.

• The dissertation proposal should outline the research scope and present initial results.
  
  
• To ensure that the dissertation has sufficient depth, yet address the intersection of IT and business, the student is encouraged to identify a primary advisor in his/her depth area, and a secondary advisor in the breadth area.

• Moreover, the dissertation committee should have representation from different research areas, as well as a mix of tenure-track and practice-track professors.

• The PhD programme committee conducts half-yearly progress review of all PhD students, and may arrange additional guidance for students where necessary.