I am an engineer from Italy. I received my BSc. in physics and information technology and MSc. in physics with the thesis titled "A Neural Network Model for Studying the Attribution of Global Circulation Atmospheric Patterns on the Climate at a Local Scale", and a second master degree in scientific computing with the thesis "Stochastic Volatility Models for European Calls Option Pricing", all from
La Sapienza University of Rome, Italy, in 2002, 2008, and 2010, respectively.In 2008 I was also Researcher with the National Research Council in Rome, where I developed neural networks models for studying the climate and its impacts on local fauna. I was a Ph.D. fellow in machine learning from 2010 to 2014 and later I became senior researcher in machine learning within the STADIUS Research Division, Department of Electrical Engineering, KU Leuven, Belgium.
I have designed advanced mathematical models to perform dynamic clustering, in order to unveil the underlying structure of data and track the evolution of patterns over time. Furthermore, I developed an adaptive clustering algorithm for real-time structural health monitoring in civil engineering systems and delivered software that predicts forthcoming faults in a packing machine based on vibration signals collected by accelerometers. Those two projects are framed within the realm of predictive maintenance and have been developed in collaboration with several academic and industrial partners.I lately joined Deloitte Belgium as senior data scientist, and later became lead data scientist. I am currently involved in the design of machine learning algorithms tailored to the clients' business needs, making sure to deliver high quality projects by coordinating and reviewing the work performed by other team members.Domains I have passion for are related to predictive maintenance, credit risk modelling, road damage detection, convolutional neural networks, advanced algorithms.