Verykios Vassilios

Extensive Curriculum Vitae

Verykios Vassilios

Professor

School of Science and Technology

Office

verykios@eap.gr

(+30) 2610367698

Curriculum Vitae

Vassilios S. Verykios received his Diploma from the University of Patras-Greece in 1992, and his MSc and PhD Degrees from Purdue University-Indiana-USA, in 1997 and 1999 respectively. From 1999 to 2001 he was an assistant professor at the College of Information Science and Technology, at the Drexel University in Philadelphia-Pennsylvania-USA. He is currently a Professor, as well as the Director of two Graduate Programs, (a) on Information Systems (PLS), and (b) on Data Science and Machine Learning (DAMA) (in English Language), in the School of Science and Technology at the Hellenic Open University (HOU). He is also the founder and the Director of the Big Data Analytics and Anonymization Lab, also at HOU. His main research interests include Data Management, Data Mining, Big Data, Parallel Systems, and Educational Data Mining. He has over than 170 publications in major refereed journals such as TKDE, DAPD, VLDB Journal, JDIQ, Information Systems, Information Sciences, etc. and in the proceedings of international conferences and workshops, while his work has received more than 10.100 citations in Google Scholar. Prof. Verykios has served on the program committees of several international scientific events such as KDD, ICDM, ECML/PKDD, and CIKM and he is currently a member of the Editorial Boards in many International Journals. He has participated as a researcher, a group leader or coordinator in several National, European and Internationally funded research and development projects, such as in an Australian Research Council’s Discovery Project, two grants from Google, and another one from SNF among many others. He has consulted for Telcordia Technologies, ChoiceMaker Technologies, Intracom SA and SingularLogic SA. He has also been a visiting researcher to CERIAS, the Department of Computer Sciences at Purdue University, the US Naval Research Laboratory and the Research and Academic Computer Technology Institute in Patras, Greece.

Research Interests

Data Management
Distributed Computing
System Software
Knowledge Discovery in Databases
Information Systems
Data and Information Quality
Data Privacy & Security
Big Data
World Wide Web services
Parallel Systems
Educational Data Mining

Teaching

PLS60 - Specialization in Software Engineering, Postgraduate Program of SST: MSc in Information Systems (PLS)
DAMA51 - Foundations in Computer Science, Postgraduate Program of SST: Data Science and Machine Learning (DAMA), (in English language)

Additional Current Academic Positions:

Director of the Postgraduate Program on Information Systems of SST, PLS (MSc in Information Systems)

Coordinatorof the Module PLS60 of the Postgraduate Program of SST, PLS (MSc in Information Systems).

Director of the Postgraduate Program of SST, DAMA (Data Science and Machine Learning)

Coordinator of the Module DAMA51 (Foundations in Computer Science), of the Postgraduate Program of SST, DAMA (Data Science and Machine Learning).

Publications

International Journals

Tsoni, R., Panagiotakopoulos, T.C., & Verykios, V.S. (2021). Revealing latent traits in the social behavior of distance learning students. Education and Information Technologies, (online). https://link.springer.com/article/10.1007%2Fs10639-021-10742-6
Paxinou, E., Kalles, D., Panagiotakopoulos, C.T., & Verykios, V.S. (2021). Analyzing Sequence Data with Markov Chain Models in Scientific Experiments. SN COMPUT. SCI. 5(2). https://doi.org/10.1007/s42979-021-00768-5
Verykios, V.S., Stavropoulos, E.C., Krasadakis, P., & Sakkopoulos, E. (2021). Frequent itemset hiding revisited: pushing hiding constraints into mining. Appl Intell. https://doi.org/10.1007/s10489-021-02490-4
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V.S. (2021). Summarizing and linking electronic health records. Distributed and Parallel Databases, 39(2), pp. 321-360. DOI: 10.1007/s10619-019-07263-0
Lazarinis, F., Alexandri, K., Panagiotakopoulos, C., & Verykios, V.S. (2020). Sensitizing young children on internet addiction and online safety risks through storytelling in a mobile application. Education and Information Technologies, 25(1), pp. 163-174. DOI: 10.1007/s10639-019-09952-w
Kyritsi, K.H., Zorkadis, V., Stavropoulos, E.C., & Verykios, V.S. (2019). The pursuit of patterns in educational data mining as a threat to student privacy. Journal of Interactive Media in Education, 2019(1):2, pp.1-10. DOI: 10.5334/jime.502
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V.S. (2018). Fast schemes for online record linkage. Data Mining and Knowledge Discovery, 32(5), pp. 1229-1250. DOI: 10.1007/s10618-018-0563-0
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V.S. (2018). FEDERAL: A Framework for Distance-Aware Privacy-Preserving Record Linkage. IEEE Transactions on Knowledge and Data Engineering, 30(2), pp. 292-304. DOI: 10.1109/TKDE.2017.2761759
Gakis, P., Panagiotakopoulos, C., Sgarbas, K., Tsalidis, C., & Verykios, V.S. (2017). Design and construction of the Greek grammar checker. Digital Scholarship in the Humanities, 32(3), pp. 554-576. DOI: 10.1093/llc/fqw025
Karapiperis, D., & Verykios, V.S. (2016). A fast and efficient Hamming LSH-based scheme for accurate linkage. Knowledge and Information Systems, 49(3), pp. 861-884. DOI: 10.1007/s10115-016-0919-y
Stavropoulos, E. C.,Verykios. V. S. and Kakglis V. A. (2015). A Transversal Hypergraph Approach for the Frequent Itemset Hiding Problem. Knowledge and Information Systems, 47(3), pp. 625-645. DOI: 10.1007/s10115-015-0862-3
Karapiperis D., Verykios V.S. An LSH-based Blocking Approach with a Homomorphic Matching Technique for Privacy-Preserving Record Linkage. IEEE Transactions on Knowledge and Data Engineering, vol. 27, issue 4, pp. 909-921, 2015
Vatsalan, D., Christen. P., O’Keefe, C. and Verykios, V.S. An Evaluation Framework for Privacy-Preserving Record Linkage, Journal of Privacy and Confidentiality, vol. 6, number 1
Vatsalan, D., Christen, P. and Verykios, V.S. A Taxonomy of Privacy-Preserving Record Linkage Techniques. Information Systems, vol. 38, no. 6, pp. 946-969, 2013
Verykios, V.S. Association Rule Hiding Methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 3, no. 1, pp. 28-36, 2013

International Conferences

Karapiperis, D., Gkoulalas-Divanis, A. & Verykios, V. S. (2021). MultiBlock: A Scalable Iterative Approach for Progressive Entity Resolution. In the Proceeddings of the 2021 IEEE International Conference on Big Data (Big Data), pp. 219-228, doi: 10.1109/BigData52589.2021.9671540
Tsoni, R., Zorkadis, V. & Verykios, V. S. (2021). A Data Pipeline to Preserve Privacy in Educational Settings. In the Proceedings of the 25th Pan-Hellenic Conference on Informatics, (in press)
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V.S. (2020).Efficient Record Linkage in Data Streams. In the Proceedings of the IEEE International Conference on Big Data, pp. 523-532. DOI: 10.1109/BigData50022.2020.9378127
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V.S. (2019). Linkage of spatio-temporal data and trajectories. In the Proceedings of the 5th IEEE International Smart Cities Conference, pp. 766-771. DOI: 10.1109/ISC246665.2019.9071724
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V.S. (2018). FEMRL: A Framework for Large-Scale Privacy-Preserving Linkage of Patients' Electronic Health Records. In the Proceedings of the IEEE International Smart Cities Conference, pp. 1-8. doi: 10.1109/ISC2.2018.8656943

Research Activities

Director of the Big Data Analytics and Anonymization Lab (BAT LAB), SST – HOU.

The objective of the Big Data Analytics and Anonymization Lab (BAT LAB) is to conduct research in the field of large-scale data management and analysis, in conjunction with privacy protection of this data, with the view to understand students’ behavior and interaction both with their peers, but also with their instructors. To this end, the desired outcome is to be able to make decisions that will benefit not only the students by improving the learning process, but will also enable the faculty and the administration to take steps towards the upgrade of the provided services. BAT LAB team consists of experienced software engineers, data scientists and mathematicians. Their aim is to take advantage of the sources of data within student and teaching population, and apply Learning Analytics techniques. Through these techniques the team focuses on detecting actionable and novel patterns, which could offer instructors a deeper understanding of the educational process resulting this way to its modernization and improvement. This process requires having experience on the techniques and the tools involved, as well as being knowledgeable about the field in which the techniques are applied.
Big Data Analytics and Anonymization Lab seeks international collaborators in the area of Privacy Preserving Data Mining, Big Data Analytics, Entity Resolution Techniques, Privacy Preserving Record Linkage, Data Science and Distance Education. We can offer our expertise and act as coordinators, consortium partners, individual experts or work package leaders.