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About

Tania Cerquitelli is a prominent researcher specializing in predictive maintenance, data-driven analytics, and intelligent systems for industrial and manufacturing applications. Her work sits at the intersection of machine learning, distributed computing architectures, and cyber-physical systems, with a particular focus on enabling smarter, more proactive approaches to industrial fault detection and failure prediction. Among her most significant contributions is her pioneering research on robotics-oriented predictive maintenance, where she developed data-driven methodologies spanning data collection through algorithmic deployment — work that has garnered 47 citations and helped establish a practical framework for the field. Building on this foundation, she has explored scalable cloud-to-edge computing strategies for predictive analytics in manufacturing environments (46 citations), and advanced microservice-based architectures that support flexible, multi-tenant deployment of smart maintenance platforms. Her investigation into fog computing further demonstrates her commitment to distributed, real-time industrial intelligence. More recently, Cerquitelli has applied machine learning to highly specialized manufacturing challenges, such as fault prediction in resistance spot welding, tackling problems where traditional testing methods are costly or destructive. With a consistent publication record addressing both theoretical frameworks and practical industrial implementation, her research has made a meaningful impact on how modern manufacturing systems anticipate and prevent operational failures.