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About

Nils Jansen is a leading researcher at the intersection of artificial intelligence, formal methods, and robotics, whose work focuses on ensuring the safety and reliability of autonomous decision-making systems. His primary research areas include safe reinforcement learning, probabilistic model checking, and strategy synthesis under uncertainty. Jansen’s most significant contribution is the development of "probabilistic shields"—a groundbreaking framework that formally guarantees safety during reinforcement learning by leveraging Markov decision processes (MDPs). His seminal 2020 paper on this topic has garnered 52 citations and established a new paradigm for trustworthy AI. He has also pioneered techniques for planning under partial observability (POMDPs) and human-robot interaction, using game-based abstractions to synthesize provably safe strategies. With over 130 total citations across his top papers, Jansen’s work has been recognized in top venues like ICAART and major robotics conferences. Notably, he has advanced the formal verification of cognitive tasks in human-robot collaboration, bridging the gap between theoretical guarantees and practical deployment. His research is essential reading for anyone working on safe autonomous systems, offering rigorous mathematical foundations for building AI that can be trusted in real-world, safety-critical environments.