Charilaos Mylonas

Charilaos Mylonas

Data/Computational Scientist, Consultant

Deloitte, Risk Data and Analytics

Biography

I am a M.Sc. and Ph.D. graduate from ETH Zurich, working at Deloitte Zurich.

link to my CV

My core skills are machine learning (deep learning), scientific computing, software engineering and I have experience through my engagements at Deloitte in financial risk management.

🎓 My deep learning-related work is on Graph Neural Networks (1), Bayesian Deep Learning for Generative modeling (e.g., (3, 4) ) and their fusion (e.g., such as VAEs for graph structured data 5, and Bayesian predictive models for time-series on graphs 6). I have also developed Bayesian deep learning models for high frequency time-series in Structural Health Monitoring (SHM) (7), and co-authored papers on using Neural ODEs for SHM (8).

These are some of the techniques I employed during my PhD at ETH Zurich.

💼 At Deloitte, I’ve worked in business development for energy market analytics, I have contributed to the assessment of liquidity and credit risk systems, and created cryptocurrency transaction analytics prototypes. I have also been involved in defining the requirements, coordinating efforts, and contributing to early asset development of retrieval augmented generation (RAG) LLM-prototypes, introduced DevOps processes, and provided several internal seminars on RAG and LLM technology.

🎹 On my free time, I play music and hack-around with microcontrollers, software engineering and automation, and keep up with deep learning research.

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

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.

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(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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Experience

 
 
 
 
 
Assistant Manager
Deloitte AG
Sep 2024 – Present
 
 
 
 
 
Senior Consultant
Feb 2022 – Aug 2024 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
 
 
 
 
 
Investment Banking Internship / 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 (e.g., trading signal discovery), implemented a R-to-C++ interface for an option pricer.