Researcher Blog by Prof Marcos Rodrigues: Leading Locally & Engaging Globally

About the author

Professor Marcos Rodrigues is Head of the Geometric Modelling and Pattern Recognition Research Group (GMPR). His research focuses on pattern recognition and sensor design for a wide variety of applications from robotics and automation, to medical engineering, security, games and animation.

In this post, Marcos explains how a new partnership between Sheffield Hallam and a major American IT company sees the University ‘leading locally and engaging globally’.

 

Recently the University Leadership Team updated staff on the new University strategy one of the key underpinning principles is ‘leading locally and engaging globally’. Here I’d like to describe an initiative between C3RI, Communication & Computing Research Centre (CCRC), Geometric Modelling and Pattern Recognition Research Group (GMPR) and the Department of Engineering and Mathematics that illustrates a contribution to these matters.

On 21 March 2017 on behalf of SHU, Professor David Waddington signed a Collaboration Agreement with PRGX, a global, NASDAQ-listed IT company based in Atlanta, US. The agreement will support our MSc students in the Department of Engineering and Mathematics. PRGX will grant scholarships worth £3,000 each on a continuous base to our students during the 3-month dissertation stage – normally June-August, October-December or February-April of each year.

The MSc dissertations will focus on the general area known as Data Science: to extract new information or knowledge from large amounts of data (structured or unstructured). The data will be provided by PRGX through granting student’s access to their secure servers. Selected students must have successfully completed the Applicable Artificial Intelligence module.

Examples of typical projects would be:

  • to analyse textual data or textual descriptions and group these into categories using Artificial Intelligence Deep Learning techniques, or
  • to perform image recognition and classification using convolution networks and deep learning techniques, or
  • related transformations to both text and images for data classification.

Expected frameworks to be investigated and exploited by individual students include:

  • TensorFlow (Google),
  • Torch (Facebook),
  • Theano Python libraries,
  • Deep Learning for Java,
  • Matlab Deep Learning,
  • Café Deep Learning Framework,
  • Digits (NVIDIA).

This is a clear example of SHU engaging globally to enhance our students’ experience and broadening our research into AI techniques with industry-relevant problems. Students will sharpen valuable data science skills that are in high demand globally, and SHU will contribute to research on this area of knowledge.

I am managing the agreement in connection with PRGX’s office in London and would like to acknowledge Lloyd Snellgrove and Michelle Hayward from the Research & Innovation Office (RIO) for their drafting of the agreement.

 

 

Please note: Views expressed are those of the Author(s) and do not necessarily reflect those of SHU, C3RI or the C3RI Impact Blog.

 

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