Dr Simon O'Keefe

Contact Details York Centre for Complex Systems Analysis University of York Heslington York YO10 5GH United Kingdom Tel: +44 (0)1904 325375 Fax: +44 (0)1904 325599 Office: RCH/329 Email username: simon.okeefe Email domain: york.ac.uk Brief CV:okeefe_CV_2013.pdf

I am a lecturer in the Department of Computer Science at the University of York.

I am a member of:

• the Advanced Computer Architecture Group
• the Non-standard Computation Group
• the York Centre for Complex Systems Analysis

Career Outline

Research interests

• EUREDIT. The aims of this project were to:
  • Develop a methodological evaluation framework and develop evaluation criteria for data editing and imputation
  • Produce a standard collection of data sets
  • Establish a baseline by evaluating currently used methods.
  • Develop and evaluate a selected range of new techniques.
  • Compare and evaluate the different methods and establish best methods for different types of data.
  • Disseminate the best methods via a single computer package and publications.
• FEDAURA. The aim of the FEDAURA Project was to develop and evaluate new computerised methods for automated Benefit fraud detection. The FEDAURA Project combined techniques from Statistics, Neural Networks and Machine Learning to produce fraud detection technologies using large-scale Benefit Claimant data sets.
• "Understanding the constraints on sex ratio adaptation in parasitoid wasps using artificial neural networks." Funded by the BBSRC, this project is bsed in Biology with Peter Mayhew.

Publications

External positions

Administrative Responsibilities

• Chair, Departmental Teaching Committee
• Summer 8-10 programme coordinator
• Hub Forum member

Previous responsibilities

• Chair, Masters Teaching Committee
• Director, YCSSA Summer School
• Chair, Mitigating Circumstances Committee
• Secretary to the Board of Studies
• Secretary to the Board of Examiners
• External student competitions co-ordinator
• Hub Management Group member

Teaching responsibilities

• Introduction Neural Computing and Applications (INCA): stage 3 module, 20 credits
• High Performance Computing (HIPC): M module, 15 credits

• Computing with Biology and Chemistry (CBCH): stage 4/M module, 10 credits
• Neural Computing (NEUC): stage 4/M module, 10 credits
• Introduction to Machine Learning (ITML) (with James Cussens and Daniel Kudenko): M module, 10 credits

And before that, modules on computer systems, computer architecture, networking and distributed systems.

PhD supervision

• Martin Rusilowicz - Technologies and Methods for the Analysis of Complex Chemometric Data (co-supervision with Julie Wilson, Chemistry/Maths)
• James Stovold - cognitive swarms (co-supervision with Jon Timmis, Electronics)

• Jobie Kirkwood - Analysis of protein crystallisation parameters (co-supervision with Julie Wilson, Chemistry)
• Nathan Burles - Pattern Recognition using Associative Memories
• Rebecca Naylor - Neural pipelines; for the coordination of activity in a multi-layered neural network
• Thomas Lampert - Spectrogram track detection: an active contour algorithm
• Quan Ju - High performance  automatic face recognition system using 3D shape information
• Nimish Shah - development of improved correlation matrix memory*
• Kelvin Hai (with Ian Benest) - ontologies for teaching and learning*
• Vicente Baez-Monroy - Neural networks as artificial memories for association rule minig
• Gavin Wood - Content-based visualisation to aid common navigation of musial audio
• Ed Tuke - Content-based semi-invariant search for natural, symbolic and sketch images

* withdrew

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This page maintained by Simon O'Keefe

Last updated: 23 October 2015