# Dr Andreas Artemiou

Senior Lecturer in Statistics

- Email:
- artemioua@cardiff.ac.uk
- Telephone:
- +44 (0)29 2087 0616
- Location:
- M2.35, Maths and Education Building , Senghennydd Road, Cardiff, CF24 4AG

- Media commentator
- Available for postgraduate supervision

I am a Senior Lecturer in Statistics at the School of Mathematics since September 2013. Before that I was an Assistant Professor at the Department of Mathematics at Michigan Technological University (2010-2013) and a New Researcher Fellow at the Statistics and Applied Mathematics Sciences Institute (SAMSI - North Carolina, US). My research has been funded by National Science Foundation, the London Mathematical Society, the GW4 Network and the Wellcome Trust.

My research interest includes high-dimensional statistics, supervised and unsupervised dimension reduction, computational statistics, machine learning and text data analysis. I am happy to discuss with prospective Ph.D. students any research projects they are interested with if they want to work with me.

### 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

### 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
- Greek Statistical Institute

### Speaking engagements

**Contributed Conference Talks/Posters: **

- 2017 European Meeting of Statisticians. Contributed Talk. “A first approach to real time dimension reduction”. Helsinki, July 2017.
- 2017 Greek Statistical Institute Annual Meeting. Contributed Talk. “Sparse Generalised Principal Component Analysis”. Cyprus, April 2017.
- 8th International Conference of the ERCIM WG on Computational and Methodological Statistics , “Inverse moments and machine learning for sufficient dimension reduction”, December 2015, London, UK
- 2015 RSS Annual meeting. Contributed Poster: “On new directions for Sufficient Dimension Reduction”, September 2015, Exeter, UK.
- 2015 EMS. Contributed Talk: “A machine learning approach for robust sufficient dimension reduction”, July 2015, Amsterdam, The Netherlands
- 2015 Multivariate Analysis Today (MATTER workshop). Contributed Poster. “Flexible Dimension Reduction in Regression”, May 2015, Milton Keynes, UK.
- 2014 RSS Annual meeting. Contributed Talk: “Sufficient dimension reduction through Support Vector Machine variants”, September 2014, Sheffield, UK.
- AG DANK/BCS 2013 meeting on variable selection and dimension reduction in clustering and classification. Contributed Talk: “Sufficient dimension reduction using support vector machines and it’s variants”, November 2013, London, UK
- Joint Statistical Meeting 2013. Contributed Talk: “Using large margin classifiers for sufficient dimension reduction”, August 2013, Montreal, Canada, August 2013.
- SAMSI Workshop on “Astrostatistics”.
*Poster presentation*: “Machine learning and Sufficient Dimension Reduction”, Research Triangle Park, September 2012 - SAMSI Opening Workshop on “Statistical and Computational Methodology for Massive Datasets”.
*Poster presentation*: “Machine learning and Sufficient Dimension Reduction”, Research Triangle Park, September 2012 - Joint Statistical Meeting 2012.
*Contributed Talk*: “Slice inverse mean difference for sufficient dimension reduction”, San Diego, CA, July 2012 - 14th meeting for New Researchers in Statistics and Probability 2012
*. Contributed Talk*: “Using machine learning algorithms in sufficient dimension reduction”, San Diego, CA, July 2012 - Workshop on Statistical Inference in Complex/High Dimensional Problems.
*Contributed Talk*: “On the use of machine learning techniques in sufficient dimension reduction”, Vienna, Austria, July 2012 - Joint Statistical Meeting 2011.
*Contributed Talk*: “Hyperplane Alignment for sufficient dimension reduction: Implementation, application, and advantages”, Miami, FL, August 2011 - Gordon Research Conferences on Quantitative Genomics 2011.
*Poster*: “Applications of hyperplane alignment on biological datasets”, Galveston, Texas, February 2011 - Joint Statistical Meeting 2010.
*Contributed Talk*: “On the predictive potential of kernel principal components”, Vancouver, Canada, August 2010 - Joint Statistical Meeting 2009.
*Contributed Poster*: “An inequality on principal components and regression”, Washington D.C., August 2009 - C. R. Rao Prize Conference 2009.
*Poster*: “An inequality on principal components and regression”, Pennsylvania State University, State College, PA, May 2009 - Joint Statistical Meeting 2008.
*Contributed Talk*: “On principal components and regression: A statistical explanation of a natural phenomenon”, Denver, CO., August 2008

** **

**Invited Conference Talks: **

- 11th International Conference of the ERCIM WG on Computational and Methodological Statistics CMStatistics. Invited Talk: Title TBA. December 2018, Pisa, Italy
- 10th International Conference of the ERCIM WG on Computational and Methodological Statistics CMStatistics. Invited Talk: “A first approach to real time and sparse real time Sufficient Dimension Reduction” , December 2017, London, UK
- Statistical Learning and Data Science Conference. Invited Talk: “Robustifying Sufficient Dimension Reduction”, Chapel Hill, NC, June 2016.
- 52nd Gregynog Statistical conference. Invited Talk: “Sufficient Dimension Reduction in Regression”, Wales, April 2016.
- International Symposium on Business and Industrial Statistics. Invited Talk: “Dimension Reduction through LqSVM”, Durham, NC, June 2014.
- 1st International Symposium of Nonparametric Statistics. Invited Talk: “Using machine learning algorithms for sufficient dimension reduction”, Halkidiki, Greece, June 2012
- Greek Statistical meeting 2011. “Hyperplane Alignment for sufficient dimension reduction: Implementation, application, and advantages”, Patra, Greece, April 2011.

** **

**Invited Colloquium/Seminar Talks: **

- “SVM-based Sufficient Dimension Reduction in Regression”, ORSTAT Unit, Faculty of Economics and Business, KU Leuven, February 2018.
- “Dimension Reduction Reduction in Regression”, DKE Seminar, COMSC, Cardiff University
- “Principal Logistic Regression for Sparse Sufficient Dimension Reduction”, Department of Mathematics and Statistics, University of Cyprus, March 2017
- “Sufficient Dimension Reduction in Regression”, Department of Mathematics, University of Bath, March 2014
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Computer Science and Engineering, European University Cyprus, May 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Mathematics and Statistics, University of Cyprus, April 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Statistics, Texas A&M University, February 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Statistics, University of South Carolina, February 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Statistics, West Virginia University, February 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Statistics, University of Missouri, February 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Mathematics Department, Syracuse University, January 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Mathematics, Tulane University, January 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Mathematics and Statistics, University of Alberta, Edmonton, January 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, Department of Actuarial Mathematics and Statistics, HeriotWatt University, January 2013
- “Sufficient Dimension Reduction through Inverse Regression and Machine Learning”, School of Mathematics, Cardiff University, December 2012
- “Utilizing machine learning in Sufficient Dimension Reduction”, Department of Statistic, University of Georgia, September 2012
- “Topics on Dimension Reduction”, Department of Statistics, London School of Economics, March 2010.
- “Topics on Dimension Reduction”, Department of Statistics, Oklahoma State University, February 2010.
- “Topics on Dimension Reduction”, Department of Mathematical Sciences, Michigan Technological University, January, 2010.

** Other Research Talks: **

- Statistics Group, School of Mathematics, Cardiff University, “Sufficient Dimension Reduction”, October 2013. A series of 2 talks
- SAMSI Undergraduate workshop on Massive Datasets. Talk on “Dimension Reduction in Regression”. Research Triangle Park, October 2012.
- Alumni Workshop, Pennsylvania State University, March 2010

**Conference Organized Invited/Chair Sessions: **

- Chair a session on “Dimension reduction and high-dimensional supervised learning” on CMStatistics conference in Pisa, December 2018
- Proposed and organized two invited sessions on “Regression and Dimension reduction for complex structures” and “Variable selection for complex data structures” to appear the annual RSS meeting in Cardiff, September 2018
- Proposed and organized an invited session on “Data Analysis of complex data structures” to appear during the 1st CRONOS MDA Conference, Limassol, Cyprus, April 2018

### Committees and reviewing

Professional Reviewing Activities

- Reviewer for the LMS Undergraduate Research Bursary competition 2019.
- Reviewer for academic Journals (among others) :
- Annals of Statistics
- Biometrika
- Biostatistics
- Computational Statistics and Data Analysis
- Journal of Applied Statistics
- Journal of Computational and Graphical Statistics.
- Journal of Machine Learning Research
- Journal of Multivariate Analysis
- Journal of Nonparametric Statistics
- Journal of Statistical Theory and Practice
- Journal of the American Statistical Association
- Journal of the Korean Statistical Society
- Journal of the Royal Statistical Society, Series B.
- PLOS
- Scandinavian Journal of Statistics
- Statistica Sinica
- Statistics and Probability Letters
- Statistics
- Technometrics
- The American Statistician
- The R Journal
- Reviewer for the Proceedings of the
- Greek Statistical Institute meetings
- International Symposiums of Nonparametric Statistics

- Reviewer for the Encyclopedia on Social Network Analysis and Mining by Springer

### 2019

- Spasic, I.et al. 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
- 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)
- Smallman, L., Underwood, W. and Artemiou, A. 2019. Simple Poisson PCA: An algorithm for (sparse) feature extraction with simultaneous dimension determination. Computational Statistics (10.1007/s00180-019-00903-0)
- 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)
- Williams, L.et al. 2019. Comparing the utility of different classification schemes for emotive language analysis. Journal of Classification (10.1007/s00357-019-9307-0)
- Artemiou, A. 2019. Cost-based reweighting for Principal Lq SVM for sufficient dimension reduction. Journal of Mathematics and Statistics
- Morgan, J.et al. 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

- Jones, B. and Artemiou, A. 2018. On principal components regression with hilbertian predictors. Annals of the Institute of Statistical Mathematics (10.1007/s10463-018-0702-9)
- 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)
- 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)
- 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)

### 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. (10.1214/154957804100000000)

### 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, pp. 1-12.
- 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.

I teach the following modules:

- MA1500 Introduction to Probability (Autumn)
- MA2501 Programming and Statistics (Spring)
- I have taught before MA0263 Introduction to Computational Statistics, MA2002 Matrix Algebra, MA3505 Multivariate Analysis

**Research Interests:**

- Supervised Dimension Reduction / Sufficient Dimension Reduction.
- Unsupervised Dimension reduction.
- Kernel methods
- Data and text mining / Support Vector Machines
- Statistical / Machine Learning
- Applications of Dimension reduction techniques in other Sciences

**Current Students and Research Assistants: **

- Luke Smallman, Ph.D., School of Mathematics, Cardiff University (Sep 2015 – Mar 2019)
- Hayley Randall, Ph.D., School of Mathematics, Cardiff University (Sep 2016- Mar 2020)
- Ben Jones, Ph.D., School of Mathematics, Cardiff University (Sep 2016- Mar 2020)
- (Co-advisor) Paul Robinson, Ph.D., Biosciences (Oct 2017 – part time)
- (Co-advisor) Ross Burton, Ph.D., Medical School (Oct 2018 – Sep 2021)
- (Co-advisor) Oliwia Michalak, Ph.D., Medical School (Oct 2018 – Mar 2022)
- Final year projects: Hector Haffenden (3rd year)

** Former Students and Research Assistants: **

- Ph.D. students:
- o Timothy Vivian-Griffiths, Ph.D. School of Medicine (co-advisor), Cardiff University Sep 2013 – April 2017)

- M.Sc theses:
- Cardiff University (2014; Konstantinos Aggelakopoulos; 2016: James Buntwal; 2017: Ben Byrne, Haimo Li; 2019 Winnie Birech)
- Lipu Tian, M.S. Mathematical Sciences (Statistics), Michigan Technological University (Graduated, May 2012. Sc. project title: “A Simulation Study on Using Moment Functions for Sufficient Dimension Reduction”)

- Min Shu, Research Assistant, January 2012 – April 2012.
- Final year project (MMath): Stefan Andjelkovic (2016)
- Final year project (BSc): Michalis Panayides (2018), Harry Chant (2018), Sarah Medland (2017), Michael Clayton-Rose (2017), Ben Byrne (2016), Laura Dimond (2016), Holly Tible (2015)
- Undergraduate Bursaries: Stephen Babos (CUROP 2018), Sophie Shapcott (CUROP 2017), Rishan Shan (funded by School of Mathematics 2015) , Alex Carney (School of Mathematics, 2014, 2015 jointly with Dr. Jennifer Morgan), Laura Dimond (School of Mathematics, 2014), Luke Smallman (London Mathematical Society, 2014)
- Undergraduate Summer Visitors: William Underwood (4 weeks in August 2017 from Oxford University)

**Funding: **

*Current: *

- Apr 2019 – Mar 2020: Welcome Trust Cardiff University ISSF3 Collaboration: Cross Disciplinary Award £49,993 led by Dr. Matthias Eberl (School of Medicine)
- Oct 2018 – Mar 2022: GW4 Biomed DTP (Doctoral Training Program) award to support a Ph.D. student in Biomedical sciences. Co-I on a grant led by Dr. Matthias Eberl. Ph.D. student Oliwia Michalak started on Oct 2018.
- Cardiff /KU Leuven Collaboration Fund Award for a week-long visit to KU Leuven to enhance my collaboration with Prof. Gerda Claeskens. (Sept 2018 to March 2019)

*Former: *

- PI on NSF DMS award 1207651 from 09/2012 to 08/2015 $110000 (Interrupted on 08/2013 due to the move to Cardiff University)
- PI on LMS Undergraduate Research Bursary award £1440 (Summer 2014)
- Jan 2018 – Dec 2018: Co-I on Cardiff University ISSF3 Collaboration: Cross Disciplinary Award £49,955 led by Dr. Matthias Eberl (School of Medicine).
- CUROP (Cardiff University’s research opportunity program) –Awarded March 2018 to supervise an undergraduate student for 8 weeks in the summer of 2018 for £2100.
- CUROP (Cardiff University’s undergraduate research program) – co-PI Awarded April 2017 to run for 8 weeks in Summer of 2017 (with Dr. Dimitris Potoglou – School of Geography and Planning, Cardiff University) for £1600.
- Supervisor on four (4) School of Math undergraduate research Bursaries; £1360 each (2 in Summer 2014 and 2 in Summer 2015)
- Michigan Technological University, 2 years startup fund (2010): ~$69000.
- **
**: PI on a submitted proposal to LMS Undergraduate Research Bursary award £1440 to run in the Summer of 2018.*Awarded but not used*