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Last updated 5th July 2024

Biography

Andrew Crampton is Professor of Computational Mathematics, Associate Dean for Teaching and Learning and a member of the Centre for Autonomous and Intelligent Systems at the University of Huddersfield

After returning to study as a mature student he completed his undergraduate degree in Mathematics and Statistics at the University of Bradford in 1999.  Andrew completed a PhD programme in approximation theory at the University of Huddersfield, UK in 2002, under the direction of Professor John Mason.

The research for Andrew’s PhD thesis mainly concentrated on the reconstruction of smooth surfaces, from noisy, scattered data using radial basis function models. The title of his thesis was "Radial Basis and Support Vector Machine Algorithms for Approximating Discrete Data.” The problems that Andrew studied included randomly scattered, structured and semi-structured data approximation using novel approximation estimators to mitigate the effects of outlier bias.  

Though his current research focuses on applications of machine learning, Andrew has successfully supervised more than ten PhD students covering areas in disc-brake vibration analysis, SCADA systems, network security, 3d printing, neuro-fuzzy inference systems and non-linear parameter estimation for feed-forward networks. Andrew has also worked with colleagues on problems concerned with automated planning, such as knowledge extraction, knowledge representation and automated planning for optimal multi-process control. 

In addition to his Associate Dean role, he teaches Computational Mathematics and Linear Algebra, and oversees the School’s Placement Unit as Academic Co-ordinator.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 16 - Peace, Justice and Strong Institutions

Research Expertise and Interests

  • Artificial Intelligence
  • Machine Learning Techniques
  • Loss Functions
  • Support Vector Machines
  • Noisy Data
  • Artificial Neural Networks
  • Radial Basis Function Networks
  • Clustering Techniques
  • Data Reduction
  • Data Classification

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