Nikolaos Pavlidis

Blog

PyTorch Geometric for Graph-Based Molecular Property Prediction using MoleculeNet benchmark | Dec 28th, 2022

My first article on Medium. In this article, I delve into the use of PyTorch Geometric, a powerful library for working with graph data, to predict various molecular properties using the MoleculeNet benchmark.

Distinction of Euclid Team in Machine Learning in 5G Challenge| Dec 20th, 2022

As a member of the Euclid team, we were proud to be recognized for our excellence in Machine Learning in 5G Challenge, organized by the International Telecommunication Union (ITU). Democritus University of Thrace official press release in Greek.

Papers

Perifanis, V., Pavlidis, N., Koutsiamanis, R. A., & Efraimidis, P. S. (2023). Federated learn-ing for 5G base station traffic forecasting. Computer Networks, 235, 109950.

Perifanis, V., Pavlidis, N., Yilmaz, S. F., Wilhelmi, F., Guerra, E., Miozzo, M., ... & Koutsiamanis, R. A. (2023, September). Towards Energy-Aware Federated Traffic Prediction for Cellular Networks. In 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC) (pp. 93-100). IEEE. (Best Paper Award Nominee). doi: 10.1109/FMEC59375.2023.10306017

Nikolaidis, C. C., Perifanis, V., Pavlidis, N., & Efraimidis, P. S. (2023, September). Federated Learning for Early Dropout Prediction on Healthy Ageing Applications. In 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC) (pp. 135-142). IEEE. doi: 10.1109/FMEC59375.2023.10306129

Pavlidis, N., Perifanis, V., Chatzinikolaou, T. P., Sirakoulis, G. C., & Efraimidis, P. S. (2023). Intelligent Client Selection for Federated Learning using Cellular Automata. In The 18th IEEE International Workshop on Cellular Nanoscale Networks and their Applications and The 8th Memristor and Memristive Symposium. IEEE.

Pavlidis, N., Nikolaidis, C. C., Perifanis, V., Papadopoulou, A., Efraimidis, P. S. & Arampatzis, A. (2023). An Extensive Overview of Feature Representation Techniques for Molecule Classification. In 27th Pan-Hellenic Conference on Progress in Computing and Informatics (PCI2023).

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About

During my five-year studies in Electrical and Computer Engineering, I have acquired a strong foundation in analytical and research skills, as well as familiarity with various programming topics and skills. My decision to pursue a PhD degree demonstrates my eagerness to continue learning and growing in this field.

Short Bio

Education

Department of Electrical and Computer Engineering, PhD
Democritus University of Thrace, Xanthi | 2022 - today
PhD Thesis: Emerging decentralized machine learning methods and their applications on graphs.

Department of Electrical and Computer Engineering, BSc, Integrated Master
Democritus University of Thrace, Xanthi | 2017 - 2022
GPA: 8.68
Diploma Thesis: Federated Learning on Graphs

High School Diploma
1st General High School Polichni, Thessaloniki | 2014- 2017
GPA: 20/20

Work Experience

Internship as software engineer
Aidplex | 2021
During my internship, I became familiar with web and mobile app development, as well as with Machine Learning application on real-world data.

Volunteering Experience

Microsoft Learn Student Ambassador (BETA)
Microsoft | 2021 - today
Member of AI & Machine Learning League, a global community of students sharing knowledge and ideas.

IEEE Brand Ambassador
IEEE | 2023 - today
Certified IEEE Brand Ambassador promoting IEEE's vision: Advancing Technology for Humanity

Mentor & Vice-Chair
IEEE DUTH STUDENT BRANCH | 2018 - 2022
My long time participation in the student team helped me acquire both soft-skills and hard-skills in different areas of interest.

Android Applications Development
Abacus Student Team DUTh | 2019
Development of various Android apps available on Play Store.

Seminars - Conferences

AZURE AI FUNDAMENTALS
Microsoft, Jun 2021

ETHICS OF AI
University of Helsinki, Feb 2021

LVIV Data science summer school
Ukrainian Catholic University, 2020

IEEE Hellenic student and young professional congress
IEEE Region 8 (EMEA) | Nov 2019

Achievements

Distinction in Machine Learning in 5G Challenge 2022
ITU - AI4Good, Dec 2022
Together with Euclid Team, we won the first place in "Federated Traffic Prediction for 5G and Beyond" Problem Statement and we came up 4th in the overall challenge.

Download my CV here

Elements

i = 0;

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    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';