Dr Andreas Artemiou

Dr Andreas Artemiou


School of Mathematics

+44 (0)29 2087 0616
M/2.35, 2nd Floor, Mathematics Institute, Senghennydd Road, Cardiff, CF24 4AG
Media commentator
Available for postgraduate supervision


  • PhD – Statistics, Pennsylvania State University, USA, 08/2010.
  • M.Sc. – Statistics, Pennsylvania State University, USA, 05/2008.
  • BSc – Mathematics and Statistics (minor Computer Science), University  of Cyprus, Cyprus, 06/2005.

Previous positions

  • 09/2012-05/2013 New Researcher Fellow at the Statistics and Applied  Mathematics Instittute
  • 08/2010 – 08/2013 Assistant Professor, Department of Mathematical  Sciences, Michigan Technological University, USA

Honours and awards

  • Eleneio Dissertation Award, Greek Statistical Institute (2011)
  • Teaching Award, Department of Statistics, Pennsylvania State  University, (2008)

Professional memberships

  • Royal Statistical Society
  • British Classification Society
  • International Association of Statistical Computing
  • Institute of Mathematical  Statistics
  • American Statistical  Association
  • Greek Statistical  Institute

Speaking engagements

Invited talks

International Symposium on Business and Industrial Statistics. Invited Talk: "Dimension Reduction through LqSVM", Durham, NC, June 2014.

1st  International Symposium on Nonparametric Statistics. "Using machine learning  for sufficient dimension reduction.&ldquo Halkidiki, Greece, June 2012

Greek Statistical  Institute meeting 2011. "Hyperplane Alignment for sufficient dimension  reduction: Implementation, Application and afdvantages", Patra, Greece, April  2011

Contributed talks

European Meeting of Statisticians 2015: "A machine learning approach for robust sufficient dimension reduction", Amsterdam, July 2015

2014 RSS Annual meeting. Contributed Talk: _Sufficient dimension reduction through Support Vector Machine variantsŒ, September 2014, Sheffield, UK.

Joint Statistical Meeting  2013: "Using large margin classifies for sufficient dimension reduction",  Montreal, Canada, August 2013

Joint Statistical Meeting  2012: "Slice inverse mean difference for sufficient dimension reduction", San  Diego, CA, July 2012

14th meeting of  New Researchers in Statistics and Probability: "Using machine algorithm in  sufficient dimension reduction", San Diego, CA, July 2012

Workshop on Statistical  Inference Complex/High Dimensional Problems: "On the use of machine learning  techniques in the sufficient dimension reduction framework" Vienna, Austria,  July 2012

Joint Statistical Meeting  2011: "Hyperplane Alignment for sufficient dimension reduction: Implementation,  Application and afdvantages", Miami, FL, August 2011

Joint Statistical Meeting  2010: "Predictive potential of Kernel Principal Support Vector Machine",  Vancouver, Canada August 2010

Joint Statistical Meeting  2008: "Principal Components and regression: A statistical explanation of a  natural phenomenos", Denver, CO, August 2008











I teach the following modules:

  • MA2002 Matrix Algebra
  • MA0263 Introduction to Computational Statistics

Postgraduate students

Students graduated (since 2000)

  • Master of Science: Lipu Tian at Michigan Technological University (2012)
  • Former Research Assistants: Min Shu at Michigan Technological University

Current students

  • Timothy Vivian-Griffiths, Ph.D., School of Medicine, Cardiff University
  • PhD student: Luke Smallman, School of Mathematics, Cardiff University (start: 10/2015)

Research interests

  • Unsupervised dimension reduction  methodology like PCA and its effectiveness when it is applied in a regression setting.
  • Supervised dimension reduction like  sufficient dimension reduction.  Using  machine learning ideas in the sufficient dimension reduction framework.
  • Machine learning algorithms.
  • Kernel methods.
  • Applications of dimension reduction and  machine learning ideas to massive/high dimensional real datasets.

External funding

Expired: US National Science  Foundation, Division of Mathematical Sciences, 09/2012 $110000

Areas of expertise

Research links