Dr Andreas Artemiou
- 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.
- 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)
- Royal Statistical Society
- British Classification Society
- International Association of Statistical Computing
- Institute of Mathematical Statistics
- American Statistical Association
- Greek Statistical Institute
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
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 variantsÂ, 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
Students graduated (since 2000)
- Master of Science: Lipu Tian at Michigan Technological University (2012)
- Former Research Assistants: Min Shu at Michigan Technological University
- Timothy Vivian-Griffiths, Ph.D., School of Medicine, Cardiff University
- PhD student: Luke Smallman, School of Mathematics, Cardiff University (start: 10/2015)
- 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.
Expired: US National Science Foundation, Division of Mathematical Sciences, 09/2012 $110000