Biography
I am a computational scientist (broad scientific computing and data science education) and I recently successfully defended my Ph.D. dissertation. During my Ph.D. work, I developed combinations of Bayesian Deep Learning and Graph Neural Networks for structural health assessment problems such as data-driven localization of damage and remaining useful life estimation.
Download my resumé.
Interests
- Probabilistic deep learing & Deep Generative Models
- Graph Neural Networks
- Scientific Computing
Education
Ph.D. in Machine Learning for SHM under Uncertainty, 2021
ETH Zurich
MSc Computational Science & Engineering, 2015
ETH Zurich
MSc in Civil Engineering, 2012
Aristotle University of Thessaloniki
Experience
Doctoral Researcher
ETH Zurich
Chair of Structural Mechanics and Monitoring. Research topics:
- Generative Models for UQ in engineering
- Graph Neural Networks
- Wind turbine and wind farm simulations
Research Assistant
ETH Zurich
Scientific Software Developer, Chair of Risk, Safety, and Uncertainty Quantification
(MSc thesis writing)
ETH Zurich
Shape optimization with Boundary Elements
Full-Stack Software Engineer
Credit Suisse
Implemented from scratch in Javascript and Python internal web-based tools for time series inspection, implemented a R-to-C++ interface for an option pricer.
Featured Publications
Recent Publications
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(2021).
Structural identification with physics-informed neural ordinary differential equations.
NeuralODEs application.
(2019).
UQLab user manual--Sensitivity analysis..
UQLab Sensitivity analysis manual.