Alessandro Abate

Delft University of Technology, University of Oxford

Papers

Total Citations

127

H-Index

About

Alessandro Abate is a prominent researcher whose work spans formal verification, control theory, reinforcement learning, and autonomous systems safety. Based at the intersection of computer science and control engineering, Abate has made significant contributions to making intelligent and safety-critical systems both efficient and provably reliable. His highly cited work on sensor scheduling for linear dynamical systems (2012, 122 citations) demonstrates his longstanding interest in optimizing how systems gather and process information under resource constraints — a foundational challenge in control theory and cyber-physical systems. More recently, Abate has turned his expertise toward the rapidly evolving field of safe reinforcement learning, tackling the critical problem of bounding safety constraint violations *during* the learning process itself — not just after training — a contribution with profound implications for deploying autonomous platforms in real-world environments. His work on runtime model verification further reflects his commitment to enabling autonomous systems to adapt dynamically without compromising safety guarantees. Across his portfolio, Abate consistently bridges rigorous theoretical foundations with practical applicability, making his research particularly valuable for students and engineers working on autonomous vehicles, robotics, and other safety-critical applications where formal guarantees are non-negotiable.