Charilaos Mylonas

Charilaos Mylonas

Computational Scientist

Deloitte, Risk Advisory


I am currently working as a senior consultant at Deloitte focusing on advanced analytics. In my prior work I developed probabilistic deep learning-based techniques for remaining useful life prediction problems in wind energy.

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  • Probabilistic deep learing & Deep Generative Models
  • Graph Neural Networks
  • Scientific Computing
  • Blockchain Analytics
  • PhD 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


Senior Consultant
Feb 2022 – Present Zurich
Implementation of advanced analytics prototypes, DevOps, and cloud computing
Doctoral Researcher
ETH Zurich
Sep 2016 – Nov 2021 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
Dec 2015 – Aug 2016 Zurich
Scientific Software Developer, Chair of Risk, Safety, and Uncertainty Quantification
(MSc thesis writing)
ETH Zurich
Dec 2014 – Aug 2015
Shape optimization with Boundary Elements
Full-Stack Software Engineer
Credit Suisse
Jul 2014 – Dec 2014
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.

Recent Publications

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(2021). Foundations of population-based SHM, Part IV: The geometry of spaces of structures and their feature spaces.

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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.

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(2019). UQLab User Manual—Canonical Low-Rank Approximations.

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