It is widely believed both in the academia and the industry that the research-practice gap is so universal and difficult to overcome. It is also widely accepted that academic research and industry practice can gain significantly from each other. Yes!mathematical models have infinite possibilities and applications in the industry.

• There is a huge gap between research and practice. The gap is real, but it can be bridged.
• To bridge the gap we need a new kind of practitioner: the translational developer. Read More.

The gap may be deliberate or accidental and extends to professional societies. In computing, most quoted challenges in bridging research-practice gap include:

• the incompatibility in timescales of research and practice
• research usually not seen as relevant for practice
• research demands a different kind of rigour than practice supports, and
• knowledge and skill sets required for research differ from that of practice.

What then can we do to bridge this gap? How can the knowledge contained in the academic society be brought to businesses? How can business and product feedbacks be utilised in the academic society to optimised existing techniques and tools or develop new ones?

These are questions being addressed in this blog, the specific interest here is on academic-industry collaboration with specific focus on our approach in teaching and practice of big data and machine learning.

This hub will offer the best, most comprehensive research and industry solutions to problems that matters to the society.