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Ming-Yang Kao is a leading figure in theoretical computer science, best known for his pioneering work in online algorithms, search theory, and optimization. His most celebrated contribution is the development of an optimal randomized algorithm for the classic "cow-path problem," which addresses how to efficiently search for a goal in an unknown environment. This work, first published in 1993 and later in a highly cited 1996 paper (garnering over 156 citations), established fundamental lower bounds and strategies for search tasks in robotics and computational geometry. Kao also made significant advances in the design of hybrid algorithms, showing how to optimally combine multiple basic algorithms to solve problems under memory constraints—a key insight for resource-limited computing environments. His 1998 paper on optimal constructions of hybrid algorithms (57 citations) remains influential in the study of online decision-making. Beyond these core contributions, Kao’s research spans computational biology, graph theory, and algorithm design, with his work consistently cited for its rigorous analysis and practical implications. His achievements have shaped how researchers approach search and optimization in uncertain environments, making him a respected authority in algorithmic theory.