I am working as a senior consultant at Deloitte within the Risk, Data, and Analytics team of Risk Advisory.

I have extensive experience with deep learning and statistical techniques through personal, academic, and work-related projects, covering a wide variety of data, such as high-frequency signals, financial data, and knowledge graphs.

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.

My current research interests revolve around machine learning for financial markets and I’ve been setting up my own small quantitative/algo-trading research and data scraping pipelines for options and stock data.

Download my resumé.