I am working as a senior consultant at Deloitte within the Risk, Data, and Analytics team of Risk Advisory.
I have extensive experience using deep learning and statistical techniques through personal, academic, and work-related projects, covering a wide variety of data, such as high-frequency signals (e.g., sensor data/simulation data/speech data), SCADA data (Wind Farm monitoring), financial data (e.g., financial risk computations), health science related data (biological signals, skin lesion images, knowledge graphs (e.g., DrugBank), protein/small molecule embeddings). Knowledge graphs and efficient large language model training are my (main) current passions!
My main expertise is on (Message-passing) 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 “irregular” time-series occuring 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.
Download my resumé.
PhD Machine Learning for SHM under Uncertainty, 2021
MSc Computational Science & Engineering, 2015
MSc in Civil Engineering, 2012
Aristotle University of Thessaloniki
Chair of Structural Mechanics and Monitoring. Research topics: