Max Robotics — Global Service Robot Directory + US Certification

About

Raffaela Mirandola is a researcher whose work sits at the intersection of self-adaptive systems, explainability, and cyber-physical systems engineering. Her research addresses one of modern software engineering's most pressing challenges: ensuring that increasingly autonomous, AI-driven systems remain transparent, trustworthy, and resilient in the face of uncertainty. Mirandola has made significant contributions to explainable self-adaptation, developing frameworks and methodologies that illuminate how black-box machine learning models drive autonomous decision-making. Her 2022 paper "XSA: eXplainable Self-Adaptation" and her 2024 work on explanation-driven self-adaptation using model-agnostic interpretable machine learning reflect a sustained commitment to making opaque adaptive systems accountable and understandable. Her 2023 conceptual framework for explainability requirements broadens this work to encompass the full spectrum of software-intensive systems, from IoT to industrial control environments. Equally impactful is her research on cyber-physical systems, where she has pioneered performance-based modelling and runtime verification approaches for resilient, QoS-aware robotic systems. Her most-cited papers have collectively accumulated over 80 citations, demonstrating meaningful influence across the software engineering and autonomous systems communities. For students and researchers navigating the challenge of building trustworthy autonomous systems, Mirandola's body of work offers both conceptual grounding and practical methodology.