Former M.Sc. students

Here you can find a bit more information about former M.Sc. students: 

  • Cardiff University (internal projects):
  1. Hantang Zhang (June 2020 – Sept 2020): Hantang worked on the development of new machine learning algorithms for classification.
  2. Zhuo Xin (June 2020 – Sept 2020): Zhuo worked on machine learning methods for credit scoring.
  3. Yilin Sun (June 2020 – Sept 2020): Yilin worked on machine learning datasets for a spam filter datasets.
  4. Min Soo Kang (June 2020 – Sept 2020): Min worked on applications of machine learning algorithms on a letter recognition dataset.
  5. Sarah Parry (Nov 2019 – Sept 2020): Sarah worked on a new machine learning algorithm for classification.
  6. Junjie He (June 2019 – Oct 2019): Junjie worked on machine learning techniques in a financial dataset.
  7. Xiangyu Wang (June 2019 – Sept 2019): Xiangyu did his work on a bat dataset which was given to the British Classification Society workshop in 2013. He worked mostly from China and he stayed there after he finished.
  8. Andreas Fredrick (June 2019 – Sept 2019): Andreas worked on the same project that Winnie Birech worked. He returned to Indonesia after he finished his MSc.
  9. Winnie Birech (Oct 2018 – Jan 2019) : Winnie worked on a dataset that was collected by Prof. Matthias Eberl group, with the help of Dr. Simone Cuff (a research associate in the group). Her task was to apply variable selection techniques to identify important factors in classifying patients with specific type of infection. She returned to Kenya after completing her M.Sc.
  10. Haimo Li (Jun 2017 – Sep 2017): Haimo worked on a dataset that was obtained from the British Classification Society. She applied machine learning and dimension reduction techniques to the dataset. She returned to China after completing her M.Sc.
  11. Ben Byrne (Jun 2017 – Sep 2017): Ben did his B.Sc. project under my guidance as well. His M.Sc. thesis was a continuation of that work. He used a dataset on eye movement which was given as a competition by RSS on it’s annual meeting in 2015 and tried to use different classification techniques for time series and functional data to analyze the dataset. After he completed his M.Sc. he got a job as a Statistical programmer with Roche Pharmaceuticals.
  12. James Buntwal (Jun 2016 – Sept 2016): James tried to apply some classification techniques to the eye movement dataset which was given as a competition on it’s annual meeting in 2015. He worked as a Data Analyst for “We Fight Any Claim” (a local company in South Wales) after the completion of his M.Sc.
  13. Konstantinos Aggelakopoulos (Feb 2016- May 2016): Konstantinos tried to apply some dimension reduction and classification methods to the Hapmap dataset, to classify the participants based on ethnic background. After he finished his M.Sc. he worked as a partner associate with Expedia, Inc in Prague, Czech Republic.
  • Michigan Technological University:
  1. Min Shu (summer research assistant – Summer 2012): As part of my start-up funding at Michigan Technological University, I hired Mrs. Min Shu (an M.Sc. student to the Department of Mathematical Sciences) to work one summer. She worked on a reweighed algorithm to address the imbalance in SVM-based dimension reduction algorithms. After completing her M.Sc. in MTU she continued with a Ph.D. in Mathematical Sciences at SUNY- Stony Brook. We have published one paper out of her work:
    • Artemiou, A. and Shu, M. (2014). “A cost based reweighted scheme on Principal Support Vector Machine“, Published in Topics of nonparametric statistics, Springer Proceedings in Mathematics and Statistics, 74, 1-22.
  2. Lipu Tian (M.Sc. Thesis – April 2012): Lipu did his M.Sc. thesis on sufficient dimension reduction methods which depend on functions of inverse means. He proposed the use of Sliced Inverse Mean Difference (SIMD). He worked as a consultant with Yahoo, Inc. after completing his M.Sc. at MTU. We have published one paper out of his work:
    • Artemiou, A. and Tian, L. (2015) “Using sliced inverse mean difference for sufficient dimension reduction“, Statistics and Probability Letters, 106, 184-190.