At Sirris, I support companies in using data to improve the reliability, efficiency and performance of their industrial assets. I develop AI and machine learning solutions for condition monitoring, anomaly detection and performance optimisation. I help companies to turn complex data streams into actionable insights, enabling predictive maintenance, early detection of underperformance and more robust operations.
My work focuses on time series analysis, context-aware models and remaining useful life forecasts. I also contribute to data innovation projects, from proposal development to project coordination.
Do you need support with industrial AI challenges, data-driven decision-making or asset health monitoring? I would be glad to assist you.
Expertise
- Industrial AI & data innovation
- Prognostics and health management
- Time series modeling
For more detailed information, please refer to my publications.
Work experience
Since 2017, I have been working as a Data Innovation Scientist at Sirris, supporting companies in applying data science and machine learning to industrial challenges.
Before that, I worked as a postdoctoral researcher in the Ansymo group at the University of Antwerp, where I also contributed as a teaching assistant in software engineering.
Studies
- PhD. in Electronic and Informatics Engineering - University of electric and electronics engineering of Cagliari (2011)
Thesis: Defect distribution in object-oriented software - Master in Electronic engineering - University of electric and electronics engineering of Cagliari (2007)
Thesis: Relationship between software graph metrics and defects propagation inside object-oriented system - Bachelor in Electronic engineering - University of electric and electronics engineering of Cagliari (2005)
Professional motto
"Even the longest journey begins with a single step."