Thao Dang

Verimag, Esslingen University of Applied Sciences

Papers

Total Citations

H-Index

About

Thao Dang is a leading researcher whose work bridges the critical intersection of formal verification, hybrid systems, and human-robot collaboration. Her foundational contributions to safety-critical systems are exemplified by her most-cited paper, "Coverage-guided test generation for continuous and hybrid systems" (2009, 86 citations), which pioneered techniques for systematically exploring the state spaces of cyber-physical systems to uncover hidden faults. This work has become essential for ensuring reliability in autonomous and embedded systems. Earlier, Dang introduced an innovative simulation-based method for validating analog and mixed-signal circuits (2006), adapting Rapidly-exploring Random Trees (RRT) from robotics to guarantee thorough coverage—a novel cross-domain application that demonstrated her ability to repurpose algorithms for new verification challenges. More recently, Dang has advanced into human-robot interaction, co-authoring "Exploiting Spatio-Temporal Human-Object Relations Using Graph Neural Networks for Human Action Recognition and 3D Motion Forecasting" (2023, 5 citations). This work leverages graph neural networks to model dynamic human-object relationships, enabling safer and more intuitive collaboration between humans and industrial robots. By combining rigorous formal methods with cutting-edge machine learning, Dang’s research continues to shape how we design, verify, and deploy intelligent systems that interact with the physical world.