PhD Student in Statistical Machine Learning

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

awards

PhD Studentship

Published:

Studentship offered by the EPSRC for the Centre of Doctoral Training in Statistics and Machine Learning (StatML CDT)

Warner Prize

Published:

Prize awarded for best MSc Statistics research project at Imperial College London.

Maths PhD Symposium 2022 Award

Published:

Runner-up & People’s Choice Award at poster competition at Imperial College London’s annual Maths PhD Symposium (2022).

Maths PhD Symposium 2025 Award

Published:

People’s Choice Award at poster competition at Imperial College London’s annual Maths PhD Symposium (2025).

publications

Benchmarking distance-based partitioning methods for mixed-type data

Published in Advances in Data Analysis and Classification, 2023

Recommended citation: @article{costa2023benchmarking, title={Benchmarking distance-based partitioning methods for mixed-type data}, author={Costa, Efthymios and Papatsouma, Ioanna and Markos, Angelos}, journal={Advances in Data Analysis and Classification}, volume={17}, number={3}, pages={701--724}, year={2023}, publisher={Springer} }
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A novel framework for quantifying nominal outlyingness

Published in Under review, 2024

Recommended citation: @misc{costa2024nominalouts, title={A novel framework for quantifying nominal outlyingness}, author={Efthymios Costa and Ioanna Papatsouma}, year={2024}, eprint={2408.07463}, archivePrefix={arXiv}, primaryClass={stat.ME}, howpublished = {arXiv preprint}, url = {https://arxiv.org/abs/2408.07463}
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A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data

Published in Under review, 2024

Recommended citation: @misc{costa2024dibmix, title={A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data}, author={Costa, Efthymios and Papatsouma, Ioanna and Markos, Angelos}, year={2024}, eprint={2407.03389}, archivePrefix={arXiv}, primaryClass={stat.ME}, howpublished = {arXiv preprint}, url = {https://arxiv.org/abs/2407.03389}
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A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data

Published in Data Science, Classification, and Artificial Intelligence for Modeling Decision Making (IFCS 2024), 2025

Recommended citation: @inproceedings{costa2024deterministic, title={A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data}, author={Costa, Efthymios and Papatsouma, Ioanna and Markos, Angelos}, booktitle={Conference of the International Federation of Classification Societies}, pages={81--88}, year={2024}, organization={Springer} }
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software

IBclust

Published:

IBclust is an R package for clustering datasets using the Information Bottleneck method and its variants. This package supports datasets with mixed-type variables (nominal, ordinal, and continuous), as well as datasets that are purely continuous or categorical. The IB approach preserves the most relevant information while forming concise and interpretable clusters, guided by principles from information theory.

SONO

Published:

SONO is an R package for computing scores of outlyingness for data sets consisting of nominal variables. It further includes various evaluation metrics for assessing performance of outlier identification algorithms producing scores of outlyingness.

talks

teaching

BSc/MSci Mathematics

Imperial College London, Department of Mathematics

  • Probability and Statistics (Year 1)
  • Probability for Statistics (Year 2)
  • Statistical Modelling I (Year 2)
  • Introduction to Statistical Learning (Years 3 & 4)
  • Mathematics of Business and Economics (Years 3 & 4)
  • Statistical Modelling II (Years 3 & 4)

MSc Statistics

Imperial College London, Department of Mathematics

  • Computational Statistics
  • Data Science
  • Machine Learning