Biography
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 both personal, academic, and work-related projects) with a wide variety of data, spanning 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, protein/small molecule embeddings), and traffic network data.
My main expertise is on Graph Neural Networks (1), Bayesian Deep Learning for Generative modeling (e.g., 3, 4 ) and their combinations (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 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.
- Probabilistic deep learning & Deep Generative Models
- (efficient) Large Language Models
- Graph Neural Networks
- Financial Risk Modeling
- Scientific Computing and Optimization
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
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Chair of Structural Mechanics and Monitoring. Research topics:
- Generative Models for UQ in engineering
- Graph Neural Networks
- Wind turbine and wind farm simulations