UoL

Dr Andreas Artemiou

Dr Andreas Artemiou

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About Dr Andreas Artemiou

Professor
School’s Dean Technology and Innovation School

 
EXPERTISE

My expertise is on statistical methods for high-dimensional and massive datasets.  I work on theoretical and computational issues of supervised and unsupervised dimension reduction, kernel methods and statistical/machine learning.  I have also an extensive network of collaborations in other sciences, including and not limited in Engineering, Computer Science, Medical Sciences, Pharmacy.

SHORT BIO

Before joining UoL, I was a Reader in Statistics at the School of Mathematics at Cardiff University where I was also the Deputy Director of the Data Science Academy. I obtained my BSc in Mathematics and Statistics from the University of Cyprus (2005) and my MSc and PhD in Statistics from Pennsylvania State University (2008, 2010).  I have worked as an Assistant Professor at Michigan Technological University (2010-2013), and as a Lecturer/Senior Lecturer/Reader at Cardiff University (2013-2023).   I have spent a year as a New Researcher Fellow in Statistics and Applied Mathematical Sciences Institute. I am an Associated Editor of the Computational Statistics and Data Analytics journal and a member of the Board of Directors of the European Section of the International Association of Statistical Computing.

Personal Information

  • Name Andreas Artemiou
  • Address Nicosia Campus: 21 Glafkou Clerides Avenue, 2107 Aglantzia Nicosia, Cyprus
  • Email artemiou@uol.ac.cy
  • Phone +357 22340383

Publications

Find more on Google Scholar

➤ Published in Statistics and related Journals:

  • Joni Virta and Andreas Artemiou (accepted July 2024) “Structure preserving non-linear PCA for matrices”, IEEE Transactions in Signal Processing.
  • Hector Haffenden and Andreas Artemiou (accepted June 2024) “Using Sliced Inverse Mean Difference for sufficient dimension reduction in multivariate time series”, Stat
  • Amarjit Gaba and Andreas Artemiou (accepted April 2024) “Poisson inverse regression for sufficient dimension reduction in text data”, Statistics and Its Interface.
  • Michalis Panayides and Andreas Artemiou (2024) “Least Squares Minimum Class Variance Support Vector Machines”, Computers, 13 (2), 34
  • Hyun Jung Jang, Seung Jun Shin and Andreas Artemiou (2023) “Principal Weighted Least Square Support Vector Machine: An Online Dimension-Reduction Tool for Binary Classification”, Computational Statistics and Data Analysis, 187,107818.
  • Antonis Christou and Andreas Artemiou (2023) “Adaptive L0 Regularization for sparse Support Vector Regression”, Mathematics, 11 (13), 2808.
  • Joni Virta and Andreas Artemiou (2023). “Poisson PCA on matrix count data”. Pattern Recognition, 131, 109401.
  • Ross Burton, Simone Cuff, Matt Morgan, Andreas Artemiou and Matthias Eberl (accepted Nov 2022) “GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data”, Bioinformatics, 39 (1), btac751  
  • Luke Smallman and Andreas Artemiou (2022) “A literature review of (sparse) exponential family PCA”, Journal of Statistical Theory and Practice, 16, 14.  
  • Eugen Pircalabelu and Andreas Artemiou (2022) “High-dimensional sufficient dimension reduction through principal projections”, Electronic Journal of Statistics, 16, 1804-1830.
  • Kimon Ntotsis, Alex Karagrigoriou and Andreas Artemiou (2021) “Interdependency pattern recognition in Econometrics: A penalized regularization antidote”, Econometrics, 9, 44
  • Eugen Pircalabelu and Andreas Artemiou (2021) “Graph informed sliced inverse regression”, Computational Statistics and Data Analysis, 164, 107302.  
  • Eliana Christou, Annabel Settle and Andreas Artemiou (2021) “Nonlinear Dimension Reduction for Conditional Quantiles”, Advances in Data Analysis and Classification, 15, 937-956.
  • Hayley Randall, Andreas Artemiou and Xingye Qiao (2021) “Sufficient Dimension Reduction based on Distance-Weighted Discrimination”, Scandinavian Journal of Statistics, 48, 1186-1211.
  • Ben Jones and Andreas Artemiou (2021) “Revisiting the predictive power of kernel principal components”, Statistics and Probability Letters, 171, 109019.
  • Andreas Artemiou, Yuexiao Dong and Seung-Jun Shin (2021). “Real-time sufficient dimension reduction through principal least squares support vector machines”, Pattern Recognition, 112, 107768.
  • Stephen Babos and Andreas Artemiou (2021). “Cumulative Median Estimation for Sufficient Dimension Reduction”, Stats (Special Issue: Robust Statistics in Action), 4, 138-145. (featured on front page)
  • Andreas Artemiou (2021) “Using mutual information to measure the predictive potential of principal components”, In Festschrift in Honor of R. Dennis Cook by Springer. Fifty Years of Contribution to Statistical Science (edited by Efstathia Bura and Bing Li), Springer, 1-15.
  • Stephen Babos and Andreas Artemiou (2020) “Sliced Inverse Median Difference Regression”, Statistical Methods and Applications, 29, 937-954 (citations: 5)
  • Luke Smallman, William Underwood and Andreas Artemiou (2020) “Simple Poisson PCA: An algorithm for (sparse) feature extraction with simultaneous dimension determination”, Computational Statistics, 35, 559-577  
  • Ben Jones and Andreas Artemiou (2020) “On principal component regression with Hilbertian predictors”, Annals of the Institute of Statistical Mathematics, 72, 627-644
  • Ben Jones, Andreas Artemiou and Bing Li (2020) “On the predictive potential of kernel principal components”, Electronic Journal of Statistics, 14, 1-23.  
  • Lowri Williams, Michael Arribas-Ayllon, Andreas Artemiou and Irena Spasic (2019). “Comparing the utility of different classification schemes for emotive language analysis”, Journal of Classification, 36, 619-648.
  • Andreas Artemiou (2019) “Cost-based reweighting for Principal LqSVM for sufficient dimension reduction”, Journal of Mathematics and Statistics – Science Publications (Special Edition on Statistical Modelling with applications), 15, 218-224.
  • Andreas Artemiou (2019). “Using adaptively weighted large margin classifiers for robust sufficient dimension reduction”, Statistics, 53, 1037-1051.  
  • Jennifer Morgan, Paul Harper, Vince Knight, Andreas Artemiou, Alex Carney and Andrew Nelson (2019) “Determining patient outcomes from patient letters: A comparison of text analysis approaches”, Journal of the OR Society, 70, 1425-1439.
  • Luke Smallman, Andreas Artemiou and Jennifer Morgan (2018) “Sparse Generalised Principal Component Analysis”, Pattern Recognition, 83, 443-455.
  • Ahmad Alothman, Yuexiao Dong and Andreas Artemiou (2018) “On dual model-free variable selection with two groups of variables”, Journal of Multivariate Analysis, 167, 366-377.  
  • Seung-Jun Shin and Andreas Artemiou (2017) “Penalized Principal Logistic Regression for Sparse Sufficient Dimension Reduction”, Computational Statistics and Data Analysis,111, 48-58
  • Luke Smallman and Andreas Artemiou (2017) “A Study on Imbalance Support Vector Machine Algorithms for Sufficient Dimension Reduction”, Communications in Statistics, Theory and Methods, 46, 2751-2763.
  • Andreas Artemiou and Yuexiao Dong (2016) “Sufficient dimension reduction via principal Lq support vector machine”, Electronic Journal of Statistics, 10, 783-805.
  • Andreas Artemiou and Lipu Tian (2015) “Using Sliced Inverse Mean Difference for Sufficient Dimension Reduction”, Statistics and Probability Letters, 106, 184-190.
  • Krystalleni Drosou, Andreas Artemiou and Christos Koukouvinos (2015) “A comparative study of the use of large margin classifiers on seismic data”, Journal of Applied Statistics, 42, 180-201.
  • Andreas Artemiou and Min Shu (2014) “A Cost Based Reweighted scheme of Principal Support Vector Machine”, Topics in Nonparametric Statistics, Springer Proceedings in Mathematics and Statistics, 74, 1-22.  
  • Andreas Artemiou (2014) “Applications of Sufficient Dimension Reduction on non-elliptical data”, Journal of the Indian Society of Agricultural Statistics, 68, 273-283 (Special issue on Statistical and Computational Methodologies on Massive Datasets)
  • 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 for linear and nonlinear sufficient dimension reduction”, Annals of Statistics, 39, 3182-3210
  • 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”, CDQM, 10(1): 110-121  

➤ Published in Journals in other Sciences:

  • Mohammad Reza et al (accepted October 2024). “Excess mortality and its determinants during the COVID-19 pandemic in 21 countries: An ecological study from the C- MOR project, 2020 and 2021”. Journal of Epidemiology and Global Health
  • Victoria Becks et al (accepted May 2024). “Cause-specific excess mortality during the COVID-19 pandemic (2020-2021): An analysis in 12 countries of the C-MOR consortium”. Journal of Epidemiology and Global Health.
  • Ross Burton et al (accepted February 2024). “Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients”. Clinical and Experimental Immunology
  • Chryso Th. Pallari et al (accepted January 2024). Magnitude and determinants of excess total, age- and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project. BMJ Global  Health. 
  • Caitlin Kneebone-Hopkins, Andreas Artemiou, Christiana A. Demetriou (2022). Determinants of excess mortality during the COVID-19 pandemic in 18 countries of the CMOR consortium, European Journal of Public Health, 32, Issue Supplement 3, ckac129.278.
  • Kerry Guest, Thomas Whalley, Jean-Yves Mallard, Andreas Artemiou, Barbara Szomolay, Mark A Webber (2022) “Responses of Salmonella biofilms to oxidising biocides: evidence of spatial clustering”, Environmental Microbiology, 24 (12), 6426-6438.
  • Mark J Ponsford, Ross J Burton,  Leitchan Smith, Palwasha Khan, Robert Andrews, Simone Cuff, Laura Tan, Matthias Eberl, Ian R Humphreys, Farbod Babolhavaeji, Andreas Artemiou, Manish Pandey, Stephen Jolles and Jonathan Underwood (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), 255-262
  • Ross Burton, Raya Ahmed, Simone M. Cuff, Sarah Baker, Andreas Artemiou, Matthias Eberl (2021) “CytoPy: an autonomous cytometry analysis framework”, PLOS Computational Biology, 17 (6), e1009071
  • Irena Spasic, David Owen, Dawn Knight and Andreas Artemiou (2019) “Unsupervised multi–word term recognition in Welsh”, In proceedings of the “Celtic Language Technology Workshop” CLTW2019, Dublin, Ireland, pages 1-6.  
  • Timothy Vivian-Griffiths, Emily Baker, Karl M Schmidt, Matthew Bracher-Smith, James Walters, Andreas Artemiou, Peter Holmans, Michael C. O’Donovan, Michael J. Owen, Andrew Pocklington, Valentina Escott-Price (2019) “Predictive modelling 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, 180B, 80-85.
  • Dimitris Challoumas and Andreas Artemiou (2018) “Predictors of attack performance in high-level male volleyball players”, International Journal of Sports Physiology and Performance, 13, 1230-1236.
  • Dimitris Challoumas, Andreas Artemiou and Georgios Dimitrakakis (2017). “Dominant vs non-dominant shoulder morphology in volleyball players and associations in pathology and spike speed”, Journal of Sports Sciences, 35, 65-73.  
  • Soumya K. Shrivastava, Andreas Artemiou and Adrienne R. Minerick (2011) “DC insulator-based dielectrophoretic characterization of erythrocytes: ABO-Rh human blood typing”, Electrophoresis, 32, 2530-2540  

➤ Peer reviewed contributions to larger publications (books, encyclopedia etc):

  • Andreas Artemiou “Regression Analysis” Contributed Essay to Encyclopedia on Social Network Analysis and Mining, Editors: Reda Alhajj, Jon Rokne, Springer (2014 – reprinted 2016). 

➤ Published Datasets and Codes:

  • Panayides, M. and Artemiou, A. (2024). Dataset and code for  “LSMCV-SVM comparisons with other SVMs”. Zenodo. doi: 10.5281/zenodo.10476188
  • Pallari, C., Demetriou, C., Achilleos, S., Artemiou, A., and Rahmanian Haghighi, M. R. (2023). Dataset and code for “Magnitude and determinants of excess total, age- and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project”. Zenodo. https://doi.org/10.5281/zenodo.8414193

Projects

1. Support Vector Machine(SVM) based Dimension Reduction;

2. CytoPy: a python statistical tool  for automated cell cytometry;

3. Participant in EU cost action on text functional and other high dimensional data in econometrics (complex and text data) (https://www.cost.eu/actions/CA21163/#tabs+Name:Working%20Groups%20and%20Membership);

4. Participant in the CMOR project which studies mortality during the COVID19 pandemic (https://www.unic.ac.cy/coronavirus/mortality/).

Contact me