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About

Matteo Camilli is a software engineering researcher whose work sits at the intersection of explainable artificial intelligence, self-adaptive systems, and cyber-physical systems. His research addresses one of the most pressing challenges in modern computing: how to make autonomous, machine learning-driven systems transparent, trustworthy, and verifiable in real-world environments. Camilli's most influential contribution, "XSA: eXplainable Self-Adaptation" (2022, 22 citations), tackles the opacity of black-box models used in self-adaptive systems, proposing frameworks that make adaptation decisions interpretable even under uncertainty. This work laid groundwork for his subsequent development of a conceptual framework for explainability requirements across software-intensive systems (2023, 11 citations), which spans enterprise, IoT, and industrial control domains. His 2024 paper on explanation-driven self-adaptation further advances model-agnostic interpretable machine learning as a practical tool for steering system behavior. Beyond explainability, Camilli investigates runtime verification for resilient cyber-physical systems and risk-driven testing for safety-critical ML applications, demonstrating a commitment to building provably dependable systems. His portfolio also extends into service robotics, reflecting a broad vision of accountable autonomous systems. With growing citation impact across multiple venues, Camilli is emerging as a significant voice in trustworthy AI-enabled software engineering.