| Affiliation | Research Associate |
| Title | PhD Candidate |
| Expertise | Bachelor’s Degree in Economics, Master’s Degree in Information Systems and Services/Big Data and Analytics |
Short CV
Efterpi Paraskevoulakou is a Research Assistant and PhD candidate at the University of Piraeus and a member of the Data & Cloud laboratory team under the supervision of Associate Professor Dr. Dimosthenis Kyriazis. She received her Bachelor’s degree from the Department of Economics of the University of Piraeus in 2017 and her Postgraduate Diploma in “Big Data and Analytics” in 2020 from the University of Piraeus/ Department of Digital Systems. Her research interests are oriented towards the orchestration and optimal management of the cloud-edge continuum applications by exploiting Artificial Intelligence, machine learning, and data analytics.
Personal Research Topics
Data Analytics / AI
Identification of data analysis problems, collection of large sets of structured and unstructured data from heterogeneous sources, purification and validation of data to ensure accuracy, completeness and uniformity, design and implementation of machine learning models and algorithms, data analysis for pattern and pattern detection, and in-depth machine learning for critical decision making.
Cloud and Edge Computing
Research and implementation -following the microservice architecture -applications using containers and modern technologies, study, and implementation of applications using the serverless architecture (Function as a Service – FaaS), utilization of modern technologies for orchestration of services in cloud environments and cloud computing environments computing (K8s Docker Compose and Swarm).
Research Projects
Scientific Publications
Journal Articles
- E. Paraskevoulakou and D. Kyriazis, “ML-FaaS: Towards exploiting the serverless paradigm to facilitate Machine Learning Functions as a Service,” in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2023.3239672.
Conference Articles
- Paraskevoulakou, E., & Kyriazis, D. (2021, March). Leveraging the serverless paradigm for realizing machine learning pipelines across the edge-cloud continuum. In 2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) (pp. 110-117). IEEE.