Research Associate with
Teaching Duties in Statistics

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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 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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Contribution to the discussion of Dümbgen and Davies (2024) ‘‘Connecting Model-Based and Model-Free Approaches to Linear Least Squares Regression’’

Published in Statistica, 2025

Recommended citation: @article{CostaPapatsouma2024, title={Discussion of the Paper "Connecting Model-Based and Model-Free Approaches to Linear Least Squares Regression" by Lutz Dümbgen and Laurie Davies (2024)}, volume={84}, DOI={10.60923/issn.1973-2201/20656}, number={2}, journal={Statistica}, author={Costa, Efthymios and Papatsouma, Ioanna}, year={2025}, pages={107–108} }
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A novel framework for quantifying nominal outlyingness

Published in Statistics and Computing, 2025

Recommended citation: @article{costa2025nominal, title={A novel framework for quantifying nominal outlyingness}, author={Costa, Efthymios and Papatsouma, Ioanna}, journal={Statistics and Computing}, volume={36}, number={41}, pages={41--58}, year={2025}, publisher={Springer} }
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Novel unsupervised techniques for mixed-type data

Published in PhD Thesis, 2026

Recommended citation: @phdthesis{costa2026thesis, title = {Novel unsupervised techniques for mixed-type data}, author = {Costa, Efthymios}, year = 2026, month = {February}, school = {Imperial College London}, DOI = {10.25560/127051}, type = {PhD thesis}}
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A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data

Published in Pattern Recognition, 2026

Recommended citation: @article{costa2026dibmix, title={A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data}, author={Costa, Efthymios and Papatsouma, Ioanna and Markos, Angelos}, journal={Pattern Recognition}, doi={10.1016/j.patcog.2026.113580}, volume={179}, number={}, pages={113580}, year={2026}, publisher={Elsevier} }
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A unified approach to outlier identification for mixed-type data

Published in Submitted, 2026

Recommended citation: @misc{costahennig2026, title={A unified approach to outlier identification for mixed-type data}, author={Costa, Efthymios and Hennig, Christian}, year={2026}, eprint={2606.26324}, archivePrefix={arXiv}, primaryClass={stat.ME}, howpublished = {arXiv preprint}, url = {https://arxiv.org/abs/2606.26324}
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Spectrally Tuned Bandwidth Selection for Kernel Fuzzy Relational Clustering

Published in Submitted, 2026

Recommended citation: @misc{costathompson2026, title={Spectrally Tuned Bandwidth Selection for Kernel Fuzzy Relational Clustering }, author={Costa, Efthymios and R.J. Thompson, John}, year={2026}, eprint={2607.03117}, archivePrefix={arXiv}, primaryClass={stat.ME}, howpublished = {arXiv preprint}, url = {https://arxiv.org/abs/2607.03117}
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Sparse clustering via the Deterministic Information Bottleneck algorithm

Published in Navigating Complexity (IFCS 2026), 2026

Recommended citation: @inproceedings{costa2026sparsedib, author = "Costa, Efthymios and Papatsouma, Ioanna and Markos, Angelos", editor = "Brito, Paula and Denti, Francesco and Greselin, Francesca and Jajuga, Krzysztof and Zenga, Mariangela", title = "Sparse Clustering via the Deterministic Information Bottleneck Algorithm", booktitle = "Navigating Complexity", year = "2026", publisher = "Springer Nature Switzerland", pages = "111--119", isbn = "978-3-032-32009-4"}
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software

IBclust

Published:

Implements multiple variants of the Information Bottleneck (‘IB’) method for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables. The package provides deterministic, agglomerative, generalised, sequential, and standard IB clustering algorithms that preserve relevant information while forming interpretable clusters. The Deterministic Information Bottleneck is described in Costa et al. (2026). The standard IB method originates from Tishby et al. (2000), the agglomerative variant from Slonim and Tishby (1999), the generalised IB from Strouse and Schwab (2017), and the sequential IB from Slonim et al. (2002). Diagnostic and plotting functions are provided to summarise, visualise, and predict from the resulting clusterings.

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