Charilaos Mylonas

Charilaos Mylonas

Computational Scientist

Deloitte, Risk Advisory

Biography

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.

Download my resumé.

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

  • 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

Experience

 
 
 
 
 
Senior Consultant
Deloitte AG
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 Posts

Literature graph navigation with tranformers
(check out my personal blog here for other projects) Table of Contents Data collection and Web Scraping Dimensionality Reduction and Literature Clustering Final thoughts Literature Clustering for Workshop I co-organized some small workshops in my department (Civil and Environmental Engineering) called “DSML@DBAUG” where researchers (Ph.
Last updated on Jun 4, 2022 5 min read
Literature graph navigation with tranformers

Events

A primer on Graph Neural Networks and Applications
Presented a 1 hour tutorial on graph neural networks and their applications (including a worked-out example for Blockchain analytics)
Mar 28, 2022 1:00 PM — 3:00 PM Online
A primer on Graph Neural Networks and Applications
Invited talk on Uncertainty, Deep Learning, and Graph Networks at RSUQ group @ ETH
Gave an extended version of my PhD presentation at the RSUQ group
Oct 28, 2021 1:00 PM — 3:00 PM
Charilaos Mylonas
Slides
Invited talk on Uncertainty, Deep Learning, and Graph Networks at RSUQ group @ ETH
Passed the Ph.D. defence (viva) with no corrections
PhD Defence passed!
Sep 28, 2021 1:00 PM — 3:00 PM
Charilaos Mylonas
Passed the Ph.D. defence (viva) with no corrections
Invited talk on GNNs and Bayesian DL in computational science
Delivered a presentation on my Graph Net and Bayesian Deep Learning work on crack detection and farm-level turbine interactions.
Sep 1, 2021 2:00 PM — 3:00 PM ETH Zurich
Slides
Invited talk on GNNs and Bayesian DL in computational science
39th IMAC - 2 GNN contributions, 1 Best Paper award!
Conference presentations on 2 works based on GNNs
Feb 1, 2021 1:00 PM — 3:00 PM
PDF
39th IMAC - 2 GNN contributions, 1 Best Paper award!
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Recent Publications

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Charilaos Mylonas, Eleni Chatzi (2021). Remaining Useful Life Estimation Under Uncertainty with Causal GraphNets.

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George Tsialiamanis, Charilaos Mylonas, Eleni Chatzi, Nicolaos Dervilis, David Wagg, Keith Worden (2021). Foundations of population-based SHM, Part IV: The geometry of spaces of structures and their feature spaces.

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Zhilu Lai, Charilaos Mylonas, Satish Nagarajaia, Eleni Chatzi (2021). Structural identification with physics-informed neural ordinary differential equations. NeuralODEs application.

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Stefano Marelli, Lamas C, Bruno Sudret, Katerina Konakli, Charilaos Mylonas (2019). UQLab user manual--Sensitivity analysis.. UQLab Sensitivity analysis manual.

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Katerina Konakli, Charilaos Mylonas, Stefano Marelli, Bruno Sudret (2019). UQLab User Manual—Canonical Low-Rank Approximations.

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