Description

I am an accomplished Research Scientist and Applied Mathematician (Ph.D) with over 6 years of experience in applying advanced methods to complex physical and industrial systems. My work lives at the intersection of Numerical modeling and physics integrating Machine Learning solutions. I translate theoretical research into high-impact solutions for industry and R&D.

How I Can Help Your Project:

Neural Networks and Physics-Informed Neural Networks (PINNs): I develop hybrid AI architectures that incorporate physical constraints (PDEs), ensuring models are consistent with the laws of nature—ideal for fluid dynamics, medical imaging (MEG/EEG), and structural engineering.
Fluid Dynamics & Numerical Simulations: Leveraging my expertise in viscous flows and particle methods, I provide expert consulting in high-fidelity simulations.
Energy Analytics (NILM): I design and implement Non-Intrusive Load Monitoring algorithms using Convolutional Neural Networks (CNN) to disaggregate complex power signals for sustainability and carbon footprint reduction.
Predictive Risk & Resilience: I build robust frameworks for disaster mitigation and structural safety, a skill set recognized with the "Best Paper Award" at the Mediterranean Geosciences Union (MedGU) in 2025.

Technical Stack:

Data Science: Python (PyTorch, Scikit-Learn, Pandas, NumPy), CNNs, Forecasting, and Statistical Modeling.

Numerical Methods: FEniCS, Finite Element, Finite Volume, Particle methods, Lattice Boltzmann Methods

Tools: Git, Linux/Ubuntu, and LaTeX for high-level technical documentation.

Based in Athens, Greece, and Rome, Italy. Available for international consulting in English, Greek, and Italian.

Industry field of expertise

Languages

Greek
Native or bilingual
English
Fluent
Italian
Fluent

Workplace preferences

Can work on-site

Rome (up to 50km)

University of Rome Tor Vergata
Senior Researcher
August 2024 - July 2025 (11 months)
Via di Tor Vergata, Rome, RM, Italy
Engineered advanced numerical simulations (Lattice Boltzmann Methods) and data analysis frameworks for biomimetic systems.
Led research on the application of bio-inspired models to enhance the sustainability and resilience of civil structures.
Developed Python-based tools to bridge the gap between biological morphology and engineering optimization.
Python Machine learning numerical methods paraview Numerical Simulation
University of L'Aquila,
Research Scientist (Seismology & ML)
July 2023 - July 2024 (1 year)
L'Aquila, AQ, Italy
Application of Machine Learning algorithms to seismic datasets to predict and classify building structural failure.
Awarded "Best Paper in Seismology Track" for innovative seismic risk mitigation modeling.
Developed predictive frameworks used for post-earthquake management and urban resilience planning.
Gran Sasso Science Institute (GSSI),
Research Fellow
November 2021 - June 2023 (1 year and 7 months)
L'Aquila, AQ, Italy
Developed finite element numerical models describing plant inspired motion of robots, with applications to autonomous environmental monitoring.
numerical methods finite element method applied mathematics