Interview

Eirini Spyropoulou

Eirini Spyropoulou works currently as data scientist at Barclays a)All statements and opinions expressed in this interview do only reflect Eirini’s personal point of view and are in no way endorsed by Barclays.. During her PhD at the University of Bristol we collaborated on multi-relational pattern discovery.


Eirini Spyropoulou is one of the most uplifting and fun collaborators I had the pleasure to work with: diligent, proficient, and yet pleasantly free of any egoic sentiment. Despite her youthful age, she already gathered plenty of valuable experience at both sides of Data Science research—the academic as well as the industrial.

Before acquiring her PhD at the University of Bristol from 2009 to 2013 she worked as professional software engineer for multiple companies. During her PhD she pushed forward the topic of subjectively interesting pattern discovery into the realm of multi-relational data [1, 2, 3]. After finishing, she put her freshly acquired scientific insights into practice by joining Toshiba Research as research engineer. During this time she remained affiliated with her former research group in Bristol and in full touch with all developments of academic community. Finally, in 2015, interluded by another period of full-time research [4, 5], Eirini then made the move to London to become data scientist at Barclays b)All statements and opinions expressed in this interview do only reflect Eirini’s personal point of view and are in no way endorsed by Barclays.. In this interview, which was originally recorded via Skype in 2015, she shares her unique perspective on Data Science in industry and academia.

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[1] E. Spyropoulou, T. De Bie, and M. Boley, “Interesting pattern mining in multi-relational data,” Data mining and knowledge discovery, vol. 28, iss. 3, pp. 808-849, 2014.
[Bibtex]
@article{spyropoulou2014interesting,
title={Interesting pattern mining in multi-relational data},
author={Spyropoulou, Eirini and De Bie, Tijl and Boley, Mario},
journal={Data Mining and Knowledge Discovery},
volume={28},
number={3},
pages={808--849},
year={2014},
publisher={Springer US}
}
[2] E. Spyropoulou, T. De Bie, and M. Boley, “Mining interesting patterns in multi-relational data with n-ary relationships,” in International conference on discovery science, 2013, pp. 217-232.
[Bibtex]
@inproceedings{spyropoulou2013mining,
title={Mining interesting patterns in multi-relational data with n-ary relationships},
author={Spyropoulou, Eirini and De Bie, Tijl and Boley, Mario},
booktitle={International Conference on Discovery Science},
pages={217--232},
year={2013},
organization={Springer Berlin Heidelberg}
}
[3] K. Kontonasios, E. Spyropoulou, and T. De Bie, “Knowledge discovery interestingness measures based on unexpectedness,” Wiley interdisciplinary reviews: data mining and knowledge discovery, vol. 2, iss. 5, pp. 386-399, 2012.
[Bibtex]
@article{kontonasios2012knowledge,
title={Knowledge discovery interestingness measures based on unexpectedness},
author={Kontonasios, Kleanthis-Nikolaos and Spyropoulou, Eirini and De Bie, Tijl},
journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
volume={2},
number={5},
pages={386--399},
year={2012},
publisher={John Wiley \& Sons, Inc.}
}
[4] J. Lijffijt, E. Spyropoulou, B. Kang, and T. De Bie, “Pn-rminer: a generic framework for mining interesting structured relational patterns,” International journal of data science and analytics, vol. 1, iss. 1, pp. 61-76, 2016.
[Bibtex]
@article{lijffijt2016pn,
title={PN-RMiner: a generic framework for mining interesting structured relational patterns},
author={Lijffijt, Jefrey and Spyropoulou, Eirini and Kang, Bo and De Bie, Tijl},
journal={International Journal of Data Science and Analytics},
volume={1},
number={1},
pages={61--76},
year={2016},
publisher={Springer International Publishing}
}
[5] M. Leeuwen, T. Bie, E. Spyropoulou, and C. Mesnage, “Subjective interestingness of subgraph patterns,” Machine learning, pp. 1-35, 2016.
[Bibtex]
@article{leeuwen2016subjective,
title={Subjective interestingness of subgraph patterns},
author={Leeuwen, Matthijs and Bie, Tijl and Spyropoulou, Eirini and Mesnage, C{\'e}dric},
journal={Machine Learning},
pages={1--35},
year={2016},
publisher={Springer US}
}

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a, b. All statements and opinions expressed in this interview do only reflect Eirini’s personal point of view and are in no way endorsed by Barclays.