Imperial College Union Student Choice Award
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Outstanding Academic Representation Network Team Award.
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Outstanding Academic Representation Network Team Award.
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Studentship offered by the EPSRC for the Centre of Doctoral Training in Statistics and Machine Learning (StatML CDT)
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Prize awarded for best MSc Statistics research project at Imperial College London.
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Runner-up & People’s Choice Award at poster competition at Imperial College London’s annual Maths PhD Symposium (2022).
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Award for the best postgraduate/doctoral paper presented at the 6th Annual Conference of the Cyprus Statistical Society.
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People’s Choice Award at poster competition at Imperial College London’s annual Maths PhD Symposium (2025).
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Highly commended nomination for the Faculty of Natural Sciences Prize for excellence in teaching and learning.
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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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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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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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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Published in Statistica (to appear), 2025
Recommended citation:
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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.
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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.
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Poster Title: “Benchmarking distance-based partitioning methods for mixed-type data”. (poster)
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Presentation Title: “Clustering mixed-type data: Which method to choose?”. (slides)
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Poster Title: “Benchmarking distance-based partitioning methods for mixed-type data”. (poster)
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Poster Title: “A novel approach to outlier detection for mixed-type data”. (poster)
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Presentation Title: “Outlier detection for mixed-type data: A novel approach”. (slides)
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Presentation Title: “A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data”. (slides)
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Presentation Title: “A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data”. (slides)
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Presentation Title: “A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data”. (slides)
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Presentation Title: “A novel framework for quantifying nominal outlyingness”. (slides)
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Presentation Title: “Utilising the Information Bottleneck algorithm for clustering mixed-type data”. (slides)
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Poster Title: “DIBmix: Information-based clustering for mixed-type data”. (poster)
Imperial College London, Department of Mathematics
Imperial College London, Department of Mechanical Engineering
Imperial College London, Department of Mathematics
Imperial College London, Department of Mathematics