Simos Gerasimou
Papers
6
Total Citations
43
H-Index
5
About
Simos Gerasimou is a researcher specializing in the formal verification, modeling, and engineering of autonomous and multi-robot systems, with a particular focus on making such systems safe, reliable, and dependable in real-world deployment. His work bridges model-driven engineering, probabilistic verification, and machine learning to address the challenges faced by robots operating in highly dynamic and safety-critical environments, such as infrastructure inspection and space exploration. Among his most notable contributions is the application of Bayesian learning to the robust verification of autonomous robots, enabling systems to continuously assess their own safety and performance under uncertainty. His research on model-driven design space exploration for multi-robot systems has provided engineers with principled tools to navigate complex configuration trade-offs, while his work on Pareto-optimal Markov decision process policy synthesis offers rigorous methods for balancing competing quality-of-service requirements. He has also advanced dependability analysis through ontological modeling frameworks tailored to automated systems. Collectively, Gerasimou's papers have garnered over 40 citations, reflecting growing recognition of his interdisciplinary contributions. His research is particularly valuable for students and practitioners working at the intersection of robotics, formal methods, and software engineering.
Research Focus
Key Achievements
Top Papers
- 1Bayesian learning for the robust verification of autonomous robots11 citations · 2024
- 2Model-Driven Simulation-Based Analysis for Multi-Robot Systems10 citations · 2021
- 3Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies7 citations · 2021
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