Badges
Certifications
Work Experience
Staff Big Data Software Engineer
Citrix•  December 2019 - Present
- Lead the maintenance and enhancements of the highly-scalable Big Data analytics components of a public Analytics Cloud Service. - R&D: Lead the design and development of high-performing Big Data processing pipelines for enhancing the application security of a public Analytics Cloud Service. - Proceed to e2e diagnostics/troubleshooting telemetry/tooling to deal with customer inquiries. Technologies/Tools: Python 3.8, BigQuery, SQL Databases
Education
University of Patras
Computer Science & Engineering, PhD•  September 2015 - June 2021
Title: Innovative Algorithmic Techniques in Cloud Computing Keywords: Cloud Computing, Distributed Processing, Social Network Analysis, Community Detection, Community Prediction, Distributed Machine Learning, Supervised Machine Learning, Classification, Bootstrap Aggregating, Bagging Ensemble Learning, Stacking Ensemble Learning, Word-Sense Disambiguation. Thesis: https://nemertes.library.upatras.gr/jspui/bitstream/10889/15160/1/Thesis.pdf Abstract: Nowadays, real-world information networks outreaching the billion vertices and have extensively been employed in a wide range of fields. Being deemed as one of the most intriguing network analysis topics, community detection has drawn the attention. The principal objective of this problem concerns the identification of groups of highly similar entities, aka communities, to which any information network might be decomposed. Therefore, with extensive application in a wide range of scientific sectors, this topic’s significance is strongly underlined. However, despite the impressive amount of research that has yet been conducted, the published methods have practically been proven inapplicable in case of contemporary social media graphs. In this regard, the purpose of this thesis is the introduction of novel and highly scalable community detection/prediction methodologies that would be capable of handling large-scale social media networks, regardless of their size and density.Nowadays, real-world information networks outreaching the billion vertices and have extensively been employed in a wide range of fields. Being deemed as one of the most intriguing network analysis topics, community detection has drawn the attention. The principal objective of this problem concerns the identification of groups of highly similar entities, aka communities, to which any information network might be decomposed. Therefore, with extensive application in a wide range of scientific sectors, this topic’s significance is strongly underlined. However, despite the impressive amount of research that has yet been conducted, the published methods have practically been proven inapplicable in case of contemporary social media graphs. In this regard, the purpose of this thesis is the introduction of novel and highly scalable community detection/prediction methodologies that would be capable of handling large-scale social media networks, regardless of their size and density.
Skills
pispirig has not updated skills details yet.