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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

Click Here to see all postgraduate research opportunities with Professor Andrew Crampton

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 Dive into the research topics where Andrew Crampton is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Brakes Engineering & Materials Science
Planning Engineering & Materials Science
Holographic interferometry Engineering & Materials Science
Machine tools Engineering & Materials Science
Motion planning Engineering & Materials Science
Pseudospectral Method Mathematics
Calibration Engineering & Materials Science
Padé Approximants Mathematics

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2004 2019

Classifying Ransomware Using Machine Learning Algorithms

Egunjobi, S., Parkinson, S. & Crampton, A., 29 Aug 2019, (Accepted/In press) 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL). 8 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

Learning algorithms
Learning systems
Malware

Information Extraction for Additive Manufacturing Using News Data

Sehgal, N. & Crampton, A., 17 Jul 2019, Advanced Information Systems Engineering Workshops: CAiSE 2019 International Workshops, Rome, Italy, June 3-7, 2019, Proceedings. Proper, H. A. & Stirna, J. (eds.). 1st ed. Cham: Springer, Cham, Vol. LNBIP 349. p. 132-138 7 p. (Lecture Notes in Business Information Processing; vol. 349).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3D printers
Industry
Semantic Web

Supporting Students to Be Global Graduates: Opportunities and Challenges in Providing an International Work-Based Learning Experience

Rimmer, F., Everson, D. & Crampton, A., 2019, (Accepted/In press) Transnational Higher Education in Computing Courses: Experiences and Reflections. Carter, J. & Rosen, C. (eds.). Cham: Springer International Publishing, p. 135-152 18 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

graduate
learning
experience
student
school
2 Citations (Scopus)
Open Access
File
Information Security
Anomaly Detection
Configuration
Testing
Audit
artificial intelligence
vulnerability
analysis
networking
art

Activities 2014 2014

  • 2 Oral presentation

ASCoL: Automated Acquisition of Domain Specific Static Constraints from Plan Traces

Rabia Jilani (Speaker), Andrew Crampton (Speaker), Diane Kitchin (Speaker), Mauro Vallati (Speaker)
15 Dec 2014

Activity: Talk or presentation typesOral presentation

Automated Knowledge Engineering Tools in Planning: State-of-the-Art and Future Challenges

Rabia Jilani (Speaker), Andrew Crampton (Speaker), Diane Kitchin (Speaker), Mauro Vallati (Speaker)
21 Jun 2014

Activity: Talk or presentation typesOral presentation