Dr Andreas Artemiou
Lecturer
- artemioua@cardiff.ac.uk
- +44 (0)29 2087 0616
- Abacws, Ffordd Senghennydd, Cathays, Caerdydd, CF24 4AG
- Sylwebydd y cyfryngau
- Ar gael fel goruchwyliwr ôl-raddedig
Bywgraffiad
Education
- 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
Anrhydeddau a Dyfarniadau
- Eleneio Dissertation Award, Greek Statistical Institute (2011)
- Teaching Award, Department of Statistics, Pennsylvania State University, (2008)
Aelodaethau proffesiynol
- Royal Statistical Society
- British Classification Society
- International Association of Statistical Computing
- Institute of Mathematical Statistics
- American Statistical Association
- Greek Statistical Institute
Ymrwymiadau siarad cyhoeddus
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 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
Cyhoeddiadau
2023
- Virta, J. and Artemiou, A. 2023. Poisson PCA for matrix count data. Pattern Recognition 138, article number: 109401. (10.1016/j.patcog.2023.109401)
- Burton, R. J., Cuff, S. M., Morgan, M. P., Artemiou, A. and Eberl, M. 2023. GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data. Bioinformatics 39(1), article number: btac751. (10.1093/bioinformatics/btac751)
2022
- Guest, K., Whalley, T., Maillard, J., Artemiou, A., Szomolay, B. and Webber, M. A. 2022. Responses of Salmonella biofilms to oxidizing biocides: evidence of spatial clustering. Environmental Microbiology 24(12), pp. 6426-6438. (10.1111/1462-2920.16263)
- Ponsford, M. J. et al. 2022. Examining the utility of extended laboratory panel testing in the emergency department for risk stratification of patients with COVID-19: a single-centre retrospective service evaluation. Journal of Clinical Pathology 75(4), pp. 255-262. (10.1136/jclinpath-2020-207157)
- Smallman, L. and Artemiou, A. 2022. A literature review of (sparse) exponential family PCA. Journal of Statistical Theory and Practice 16, article number: 14. (10.1007/s42519-021-00238-4)
- Pircalabelu, E. and Artemiou, A. 2022. High-dimensional sufficient dimension reduction through principal projections. Electronic Journal of Statistics 16(1), pp. 1804-1830. (10.1214/22-EJS1988)
2021
- Randall, H., Artemiou, A. and Qiao, X. 2021. Sufficient dimension reduction based on distance-weighted discrimination. Scandinavian Journal of Statistics 48(4), pp. 1186-1211. (10.1111/sjos.12484)
- Christou, E., Settle, A. and Artemiou, A. 2021. Nonlinear dimension reduction for conditional quantiles. Advances in Data Analysis and Classification 15, pp. 937-956. (10.1007/s11634-021-00439-6)
- Pircalabelu, E. and Artemiou, A. 2021. Graph imposed sliced inverse regression. Computational Statistics & Data Analysis 164, article number: 107302. (10.1016/j.csda.2021.107302)
- Ntotsis, K., Karagrigoriou, A. and Artemiou, A. 2021. Interdependency pattern recognition in econometrics: a penalized regularization antidote. Econometrics 9(4), article number: 44. (10.3390/econometrics9040044)
- Burton, R. J., Ahmed, R., Cuff, S. M., Baker, S., Artemiou, A. and Eberl, M. 2021. CytoPy: An autonomous cytometry analysis framework. PLoS Computational Biology 17(6), article number: e1009071. (10.1371/journal.pcbi.1009071)
- Artemiou, A., Dong, Y. and Shin, S. J. 2021. Real-time sufficient dimension reduction through principal least squares support vector machines. Pattern Recognition 112, article number: 107768. (10.1016/j.patcog.2020.107768)
- Jones, B. and Artemiou, A. 2021. Revisiting the predictive potential of Kernel principal components. Statistics and Probability Letters 171, article number: 109019. (10.1016/j.spl.2020.109019)
- Babos, S. and Artemiou, A. 2021. Cumulative median estimation for sufficient dimension reduction. Stats 4(1), pp. 138-145. (10.3390/stats4010011)
- Artemiou, A. 2021. Using mutual information to measure the predictive power of principal components. In: Li, B. and Bura, E. eds. Festschrift to Dennis Cook. Springer
2020
- Smallman, L., Underwood, W. and Artemiou, A. 2020. Simple Poisson PCA: An algorithm for (sparse) feature extraction with simultaneous dimension determination. Computational Statistics 35, pp. 559-577. (10.1007/s00180-019-00903-0)
- Jones, B. and Artemiou, A. 2020. On principal components regression with hilbertian predictors. Annals of the Institute of Statistical Mathematics 72, pp. 627-644. (10.1007/s10463-018-0702-9)
- Babos, S. and Artemiou, A. 2020. Sliced inverse median difference regression. Statistical Methods and Applications 29, pp. 937-954. (10.1007/s10260-020-00509-7)
- Jones, B., Artemiou, A. and Li, B. 2020. On the predictive potential of kernel principal components. Electronic Journal of Statistics 14(1), pp. 1-23. (10.1214/19-EJS1655)
2019
- Williams, L., Arribas-Ayllon, M., Artemiou, A. and Spasic, I. 2019. Comparing the utility of different classification schemes for emotive language analysis. Journal of Classification 36(3), pp. 619-648. (10.1007/s00357-019-9307-0)
- Artemiou, A. 2019. Using adaptively weighted large margin classifiers for robust sufficient dimension reduction. Statistics 53(5), pp. 1037-1051. (10.1080/02331888.2019.1636050)
- Artemiou, A. 2019. Cost-based reweighting for Principal Lq SVM for sufficient dimension reduction. Journal of Mathematics and Statistics 15(1), pp. 218-224. (10.3844/jmssp.2019.218.224)
- Spasic, I., Owen, D., Knight, D. and Artemiou, A. 2019. Unsupervised multi-word term recognition in Welsh. Presented at: Celtic Language Technology Workshop 2019, Dublin, Ireland, 19 August 2019 Presented at Lynn, T. et al. eds.Proceedings of the Celtic Language Technology Workshop. European Association for Machine Translation
- Vivian-Griffiths, T. et al. 2019. Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 180(1), pp. 80-85. (10.1002/ajmg.b.32705)
- Morgan, J., Harper, P., Knight, V., Artemiou, A., Carney, A. and Nelson, A. 2019. Determining patient outcomes from patient letters: A comparison of text analysis approaches. Journal of the Operational Research Society 70(9), pp. 1425-1439. (10.1080/01605682.2018.1506559)
2018
- Challoumas, D. and Artemiou, A. 2018. Predictors of attack performance in high-level male volleyball players. International Journal of Sports Physiology and Performance 13(9), pp. 1230-1236. (10.1123/ijspp.2018-0125)
- Alothman, A., Dong, Y. and Artemiou, A. 2018. On dual model-free variable selection with two groups of variables. Journal of Multivariate Analysis 167, pp. 366-377. (10.1016/j.jmva.2018.06.003)
- Smallman, L., Artemiou, A. and Morgan, J. 2018. Sparse generalised principal component analysis. Pattern Recognition 83, pp. 443-455. (10.1016/j.patcog.2018.06.014)
2017
- Shin, S. J. and Artemiou, A. 2017. Penalized principal logistic regression for sparse sufficient dimension reduction. Computational Statistics & Data Analysis 111, pp. 48-58. (10.1016/j.csda.2016.12.003)
- Smallman, L. and Artemiou, A. 2017. A study on imbalance support vector machine algorithms for sufficient dimension reduction. Communications in Statistics - Theory and Methods 46(6), pp. 2751-2763. (10.1080/03610926.2015.1048889)
- Challoumas, D., Artemiou, A. and Dimitrakakis, G. 2017. Dominant vs non-dominant shoulder morphology in volleyball players and associations with shoulder pain and spike speed. Journal of Sports Sciences 35(1), pp. 65-73. (10.1080/02640414.2016.1155730)
2016
- Artemiou, A. and Dong, Y. 2016. Sufficient dimension reduction via principal Lq support vector machine. Electronic Journal of Statistics 10(1), pp. 783-805. (10.1214/16-ejs1122)
2015
- Artemiou, A. and Tian, L. 2015. Using sliced inverse mean difference for sufficient dimension reduction. Statistics and Probability Letters 106, pp. 184-190. (10.1016/j.spl.2015.07.025)
- Drosou, K., Artemiou, A. and Koukouvinos, C. 2015. A comparative study for the use of large margin classifies on seismic data. Journal of Applied Statistics 42(1), pp. 180-201. (10.1080/02664763.2014.938619)
2014
- Artemiou, A. and Shu, M. 2014. A cost based reweighted scheme of Principal Support Vector Machine. In: Akritas, M. G., Lahiri, S. N. and Politis, D. N. eds. Topics in Nonparametric Statistics. Springer Proceedings in Mathematics & Statistics Springer, (10.1007/978-1-4939-0569-0_1)
- Artemiou, A. 2014. Applications of sufficient dimension reduction algorithms on non-elliptical data. Journal of the Indian Society of Agricultural Statistics 68(2), pp. 273-283.
2013
- Artemiou, A. and Li, B. 2013. Predictive power of principal components for single-index model and sufficient dimension reduction. Journal of Multivariate Analysis 119, pp. 176-184. (10.1016/j.jmva.2013.04.015)
2011
- Srivastava, S. K., Artemiou, A. and Minerick, A. R. 2011. Direct current insulator-based dielectrophoretic characterization of erythrocytes: ABO-Rh human blood typing. Electrophoresis 32(18), pp. 2530-2540. (10.1002/elps.201100089)
- Li, B., Artemiou, A. and Li, L. 2011. Principal support vector machines for linear and nonlinear sufficient dimension reduction. Annals of Statistics 39(6), pp. 3182-3210. (10.1214/11-AOS932)
2009
- Artemiou, A. and Li, B. 2009. On principal components and regression: a statistical explanation of a natural phenomenon. Statistica Sinica 19, pp. 1557-1565.
2007
- Artemiou, A. 2007. Hypothesis testing in frailty models for arbitrarily censored and truncated data. Communications in Dependability and Quality Management 10(1), pp. 110-121.
Addysgu
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