Claudio Sbarufatti (MSc in Mechanical Engineering, Politecnico di Milano, 2009; PhD in Aerospace Engineering - Rotary-Wing Aircrafts, Politecnico di Milano, 2013) is associate professor (ING-IND/14) at the Department of Mechanical Engineering at the Politecnico di Milano since November, 2019 and is Professor of the courses Machine Design 1, Digital-Twin for industrial/energy system management and Methods for system health monitoring and prognosis. 
His main expertise is focused on the development of methods for health and usage monitoring of structural components and systems. The monitoring methodology leverages on the combination of sensor data and models (Digital-Twin) for the identification of characteristic patterns in the signals, enabling the diagnosis and prognosis of system degradation.
Within this context, his research interests include the development and application of statistical and numerical methods for the solution of inverse problems in the context of structural anomaly and structural load identification, inverse FEM, physics-informed machine learning, Bayesian inference and Monte-Carlo methods for model updating, damage prognosis of composite structures, sensor network optimization, active and passive impact monitoring and, more recently, the identification of multifunctional composite materials with self-sensing and self-heating functions. 
He is head of the research line Structural Health Monitoring and Prognosis in the SIGMALab and is currently involved in the management of national and international projects, the latter in the framework of the European Defence Agency, mainly focused on digital-twin modelling, load and structural health monitoring and residual life prognosis for predictive maintenance of component and systems. He is currently supervisor of 8 PhD students, two of which under the ENHANCE project (call H2020-MSCA-ITN-2019).
He is co-author of more than 100 papers (Hindex 18 – source Scopus) on international journals and proceedings of international conferences. He obtained two scientific awards in the field of damage diagnosis and prognosis.