Areas of Expertise

  • Machine Learning
  • Data Science
  • Wearables (EEG)
  • Applied Deep Learning
  • Signal Processing

Publications

  1. & Computer Vision Techniques for Transcatheter Intervention. IEEE Journal of Translational Engineering in Health and Medicine 3, 1-31.
  2. & 'Image Characterisation' Patent number WO0239386. BT Technology Journal 20(4), 145-147.
  3. & Visual Form 2001. Lecture notes in computer science
  4. & Motion-based classification of cartoons. (Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489)).
  5. & Acoustic and facial features for speaker recognition. (Proceedings 15th International Conference on Pattern Recognition. ICPR-2000).

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Teaching

  • CS-001 Fundamental Mathematics Skills for Natural Scientists.

    This module introduces students to the basic techniques of pre-calculus mathematics as well as statistics relevant to their particular degree scheme,

  • CSC005 Computational Probability

    This module gives students an understanding of Monte Carlo methods, which use random simulation to computationally solve physical problems which may be difficult if not impossible to solve analytically. The delivery style of the module is very much tutorial in nature: only basic programming and probability theory will be taught and required, with the module concentrating on practical weekly lab-based problem-solving sessions.

  • CSCM10 Computer Science Project Research Methods

    This module will introduce students to some fundamental research methodologies and good practice in research. They will undertake background research including a literature review and specify the aims of their MSc project.

  • CSDM001 Theory of Digital Information and Society

    This module gives an overview on theoretical and methodological debates contemporary Digital Economy and Society research with specific focus on Human Computer Interaction. This module helps students understand how human experience can influence the design and adoption of digital into services and the lives of individuals and communities.Students explore the advanced literature and research results underpinning the field. Understand, through a series of Classic papers, the practical application of qualitative and quantitative techniques for the study of soci-technical assemblages in a digital by default world, as well as recent work from the leading figures. Students achieve a clear view of the 'cutting edge' and issues in the field and where things are happening. The module is very interactive, and students will be expected to give presentations.

  • CSDM03 Computational Thinking skills for Digital Social Scientists.

    This module will discuss some of the most widely used and artificial intelligence and machine learning, regression & clustering techniques and their applications to big data social science questions. The students will gain and understanding of both strengths and weaknesses of learning and practical know-how in applying those theories to real world problems. Topics include big social data concepts, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.

  • MA-009 Computational Probability

    This module gives students an understanding of Monte Carlo methods, which use random simulation to computationally solve physical problems which may be difficult if not impossible to solve analytically. The delivery style of the module is very much tutorial in nature: only basic programming and probability theory will be taught and required, with the module concentrating on practical weekly lab-based problem-solving sessions.

Supervision

  • Adopting data driven modelling and prediction approaches to support strategies for successful game outcomes in Rugby (current)

    Student name:
    MSc
    Other supervisor: Dr Xianghua Xie
  • Emotional experiences of and Reactions to Night-time Interventions in Cities in the Example of Swansea Police«br /»«br /»«br /»«br /»«br /» (current)

    Student name:
    PhD
    Other supervisor: Dr Sergei Shubin