Kar‐Ann Toh

Papers

Total Citations

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About

Kar-Ann Toh is a researcher whose work bridges the theoretical foundations of deep learning with practical robotics applications. His key research areas include explainable artificial intelligence, robot kinematics, and biometric security for mobile systems. Toh’s most notable contribution is the development of an analytic layer-wise deep learning framework, which addresses the critical need for theoretical understanding in black-box models—a work that has garnered 23 citations and contributes to the emerging field of explainable AI. He has also advanced robotics through a data-driven iterative learning algorithm for kinematic approximation, offering a robust alternative to traditional parametric models. In the domain of mobile security, Toh has contributed to biometric security, editing a special issue that underscores the growing importance of cryptography and secure authentication in mobile computing environments. His work reflects a commitment to making AI not only more powerful but also more interpretable and reliable, with applications ranging from industrial robotics to secure mobile systems.