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About

Ilias Gerostathopoulos is a researcher specializing in self-adaptive systems, cyber-physical systems (CPS), and autonomous software architectures. His work sits at the intersection of software engineering, artificial intelligence, and robotics, addressing one of modern computing's most pressing challenges: how complex systems can reliably operate under real-world uncertainty. Among his notable contributions, Gerostathopoulos has advanced the theoretical foundations of autonomous systems through taxonomic frameworks, while also delivering practical tools for the research community — including a widely adopted model problem and testbed for smart CPS experimentation (2016, 9 citations). His research on subjective logic for run-time reasoning under uncertainty (2021, 9 citations) pushes the boundaries of how multi-agent systems handle unpredictable environments, with direct relevance to drones, robots, and self-driving vehicles. More recently, Gerostathopoulos has focused on robotics self-adaptation, producing influential work on architecture-based approaches and behavior trees — exemplified by the ReBeT framework (2024) — that enable robots to dynamically reconfigure at runtime. His machine learning approach to ensemble formation (2020, 7 citations) further demonstrates his versatility across methods. With a growing citation record spanning foundational theory to deployable systems, his research offers valuable guidance for engineers building the next generation of intelligent, resilient autonomous systems.