Daniel Große
Papers
2
Total Citations
5
H-Index
2
About
Daniel Große is a leading researcher in the formal verification of complex, autonomous systems, with a particular focus on ensuring the safety and correctness of next-generation devices. His work bridges the gap between hardware verification and real-world robotic applications, tackling the critical challenge of validating systems that must operate reliably in unpredictable environments. Große’s major contributions include pioneering logic-based environment modeling to verify safety properties of robotic plans, as demonstrated in his 2020 paper (3 citations), which provides a rigorous framework for ensuring autonomous robots behave correctly outside of controlled lab settings. His broader vision is captured in his 2019 work (2 citations) on ensuring correctness from reconfigurable hardware to self-learning systems, addressing the escalating complexity of devices used in autonomous driving and robotics. While his citation counts reflect the emerging nature of this field, Große’s research is foundational for building trustworthy AI-driven systems. He is a key figure in advancing verification methodologies that will enable the safe deployment of adaptive, autonomous technologies in our everyday world.
Research Focus
Key Achievements
Top Papers
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- 2