Meirav Zehavi

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About

Meirav Zehavi is a leading researcher in parameterized complexity and temporal graph algorithms, whose work bridges theoretical computer science and real-world robotics. Her most-cited paper, “In Which Graph Structures Can We Efficiently Find Temporally Disjoint Paths and Walks?” (2023, 4 citations), tackles a fundamental challenge in temporal networks: computing paths that avoid vertex-time conflicts. This research has direct implications for scheduling, communication networks, and logistics, where time-varying connectivity is critical. Zehavi also explores motion planning in “The Parameterized Complexity of Motion Planning for Snake-Like Robots” (2019, 2 citations), where she models the movement of linked agents—inspired by the classic game Snake—as a parameterized problem. This work addresses real-world scenarios such as autonomous convoy navigation and multi-agent transport. Her contributions are distinguished by their focus on efficient algorithmic solutions for problems that are computationally hard in general, offering structural insights that enable tractability. With a growing citation record and a reputation for tackling both foundational and applied problems, Zehavi is a rising voice in the intersection of graph theory, parameterized complexity, and robotics.