Dr Andreas Artemiou
Telephone: +44(0)29 208 70616
Fax: +44(0)29 208 74199
- 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.
MA2002 Matrix Algebra
MA0263 Introduction to Computational Statistics
Peer reviewed articles:
Andreas Artemiou and Min Shu (2013). Cost reweighed scheme for principal support vector machine for sufficient dimension reduction. To appear in contemporary development in Statistical Theory
Andreas Artemiou and Bing Li (2013). Predictive power of principal components for single index model and sufficient dimension reduction. Journal of Multivariate Analysis, 119, 176-184.
Bing Li, Andreas Artemiou and Lexin Li (2011. ) Principal Support Vector Machine. Annals of Statistics, 39, 3182 – 3210.
Soumya Shrivastava, Andreas Artemiou and Adrienne Minerick (2011). DC insulator based dielectrophoretic characterization of erythrocytes: ABO-Rh human blood typing. Electrophoresis, 32, 2530-2540.
Andreas Artemiou and Bing Li (2009). On principal components and regression: A statistical explanation of a natural phenomenon. Statistica Sinica, 19, 1557-1565.
Filia Vonta and Andreas Artemiou (2007). Hypothesis testing in frailty models for arbitrary censored and truncated data. Communications in Depandability, Quality and Management, 10, 110-121.
Contributed Essay on Regression Analysis to appear in Encyclopedia on Soial Network Analysis and Mining by Springer.
US National Science Foundation, Division of Mathematical Sciences, 09/2012 $110000
Major Conference Talks
1st International Symposium on Nonparametric Statistics. “Using machine learning for sufficient dimension reduction. Halkidiki, Greece, June 2012
Greek Statistical Institute meeting 2011. “Hyperplane Alignment for sufficient dimension reduction: Implementation, Application and afdvantages”, Patra, Greece, April 2011
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
Students Graduated (Since 2000)
Master of Science:
Lipu Tian at Michigan Technological University (2012)
Former Research Assistants:
Min Shu at Michigan Technological University
Ph.D. students: James Wright at Michigan Technological University (expected graduation May 2014)
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
Eleneio Dissertation Award, Greek Statistical Institute (2011)
Teaching Award, Department of Statistics, Pennsylvania State University, (2008)
Institute of Mathematical Statistics
American Statistical Association
Greek Statistical Institute