Andrew Crampton — University of Huddersfield Research Portal

Personal profile

Biography

I returned to study as a mature student in 1996 when I started a degree in Mathematics and Statistics at the University of Bradford. After graduating with a first class honours in 1999, I started a PhD programme in approximation theory in the School of Computing and Engineering at the University of Huddersfield, under the direction of Professor John Mason.

The research for my PhD thesis mainly concentrated on the reconstruction of smooth surfaces, from noisy scattered data using radial basis function models. The title of my thesis was "Radial Basis and Support Vector Machine Algorithms for Approximating Discrete Data.” The problems that I studied included randomly scattered, structured and semi-structured data approximation, support vector machines with radial kernels (for mixed noise distributions) and detecting and approximating fault lines using discrete Gaussian curvature and locally constructed thin plate spline interpolants.

I completed my PhD in 2002 and after spending three years as a research fellow became a full time senior lecturer. In 2011 I became the Subject Area Leader for Computing and Information Systems in the Informatics Department and was Acting Head of Department of Informatics from June 2015 until January 2017. In May 2017 I became Associate Dean (T&L).

Research Expertise and Interests

My research falls broadly into the area of Artificial Intelligence. I am particularly interested in machine learning techniques - in particular I am interested in the use of various loss functions used in support vector machines for the treatment of noisy data (using novel approximation estimators to mitigate the effects of outlier bias). I also research other supervised and unsupervised learning techniques which includes artificial neural networks (in particular radial basis function networks), clustering techniques, data reduction and data classification. I also work with colleagues on problems concerned with automated planning, such as knowledge extraction, knowledge representation and automated planning for optimal multi-process control.

Research Degree Supervision

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Keywords

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

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

Planning Engineering & Materials Science

Machine tools Engineering & Materials Science

Motion planning Engineering & Materials Science

Brakes Engineering & Materials Science

Pseudospectral Method Mathematics

Calibration Engineering & Materials Science

Padé Approximants Mathematics

Dynamometers Engineering & Materials Science

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Research Output 2005 2018

13 Conference contribution
10 Article
2 Chapter

GraphBAD: A General Technique for Anomaly Detection in Security Information and Event Management

Parkinson, S., Vallati, M., Crampton, A. & Sohrabi, S. 28 Jan 2018 In : Concurrency Computation Practice and Experience. 16 p.

Research output: Contribution to journal › Article

Information Security

Anomaly Detection

Configuration

Testing

Audit

A Multi-layered Cloud Protection Framework

Khan, S., Parkinson, S. & Crampton, A. 5 Dec 2017 Proceedings of the 10th International Conference on Utility and Cloud Computing (UCC), (Austin, TX: 5-8 December 2017): Companion Volume for Workshop Materials. Association for Computing Machinery (ACM), p. 233-238 6 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cloud computing

Access control

Authentication

Defects

Industry

Have a little patience: Let planners play cards

Jilani, R., Crampton, A., Kitchin, D. & Vallati, M. 2017 Proceedings of the 34th Workshop of the UK Planning and Scheduling Special Interest Group: (PlanSIG 2016). Chrpa, L., Parkinson, S. & Vallati, M. (eds.). Vol. 1782, 7 p. (UK Planning and Scheduling Special Interest Group 2016.)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access

Planning

Novel Intrusion Detection Mechanism with Low Overhead for SCADA Systems

Maglaras, L., Janicke, H., Jiang, J. & Crampton, A. 2017 Security Solutions and Applied Cryptography in Smart Grid Communications: Advances in Information Security, Privacy, and Ethics. Ferrag, M. A. & Ahmim, A. (eds.). IGI Global, p. 160-178 19 p. Chapter 9. (Security Solutions and Applied Cryptography in Smart Grid Communications)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Open Access

SCADA systems

Intrusion detection

Support vector machines

Critical infrastructures

Testbeds

2 Citations