I joined the Department of Economics, Statistics, and Actuarial Sciences in 2021 as a researcher and lecturer. My current research is funded by the Italian government's project DM n. 1062/2021 PON (research on green and innovation themes) on the topic of energy markets forecasting with econometrics models.

My research interests are in the areas of econometrics, statistics, and machine learning. I am actively exploring the applicability of Bayesian methods within Machine Learning (ML) as a successful approach for econometrics and finance. In particular, my current research focuses on a sub-class of Deep Learning algorithms known as Bayesian Neural Networks. These are particularly attractive since they embed the desirable and robust probabilistic properties typical of econometric modeling and the great flexibility typical of ML algorithms. The challenges towards this research direction are manifold across modern Bayesian statistics, state-of-the-art ML methodologies, high-frequency econometrics modeling, and the ubiquitous big-data issues underlying all the empirical analyses. 

I am working on developing models and exploring different solutions for a variety of classification and prediction problems from both a theoretical and applied perspective (e.g., practical and effective Variational Inference algorithms), with an eye on the needs of the financial industry as well.

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