Foteini Dervisi

Foteini Dervisi’s biography

• 2022 to ongoing: Marie Skłodowska-Curie early stage researcher, British Geological Survey
• 2022 to ongoing: PhD in Geology and geophysics, University of Edinburgh
• 2023 to ongoing: Tutor/demonstrator, University of Edinburgh
• 2022 to 2022: Machine learning engineer, Pragma-IoT
• 2021 to 2022: Research assistant, Information Technologies Institute, Centre for Research and Technology Hellas
• 2020 to 2022: MSc in Artificial intelligence and data analytics, University of Macedonia
• 2019 to 2019: Data scientist intern, NET2GRID
• 2014 to 2019: Diploma (BSc & integrated MSc) in Electrical and computer engineering, Aristotle University of Thessaloniki

Research projects

• SPIN (Seismological Parameters and Instrumentation): Monitoring a Restless Earth – Horizon 2020 Marie Skłodowska-Curie Actions Innovative Training Network funded by the European Commission focusing on advancing seismic observation, theory and hazard assessment

Research interests

• Machine learning & data science
• Real-world AI applications:
  • Natural hazard management
  • Climate change mitigation and adaptation
  • Earth observation
  • Environmental monitoring

ORCID: 0000000262573707

Journal articles

• Vavouris, A K, Dervisi, F D, Papanikolaou, V K, Diamantoulakis, P D, Karagiannidis, G K, and Goudos, S K. 2019. An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks. Technologies, 7 (1):14.

Conference papers

• Dervisi, F, Kyriakides, G, and Margaritis, K. 2022. Evaluating Acceleration Techniques for Genetic Neural Architecture Search. In: Iliadis, L, Jayne, C, Tefas, A, and Pimenidis, E (eds). Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham.
• Vavouris, A K, Dervisi, F D, Papanikolaou, V K and Karagiannidis, G K. 2018. An energy efficient modulation scheme for body-centric nano-communications in the THz band.  7th International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, 2018, pp. 1–4, doi: 10.1109/MOCAST.2018.8376563.

Conference abstracts

• Dervisi, F, Segou, M, Baptie, B, Main, I, and Curtis, A. 2024. Towards a Deep Learning Approach for Data-Driven Short-Term Spatiotemporal Earthquake Forecasting. EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17178.

Theses

• MSc thesis: Dervisi F. Evaluating acceleration techniques for candidate evaluation in genetic neural architecture search, University of Macedonia, 2022,
• MEng thesis: Vavouris A, and Dervisi F. Hybrid mmWave / Free Space Optical Design with the use of NOMA techniques for 5G Backhaul Networks, Aristotle University of Thessaloniki, 2019, (in Greek)

Google scholar – Foteini Dervisi

• Electrical, telecommunication and computer engineering
• Machine learning and data science
• Python programming
• Scientific writing
• Fluent in Greek (native language)
• Knowledge of French, German and Italian

• Member of the Technical Chamber of Greece
• Member of the European Geosciences Union (EGU)