Dalal Alrowaili

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About

Dalal Alrowaili is a mathematician whose research lies at the intersection of graph theory, network science, and combinatorial optimization. Her primary focus is on the metric dimension of graphs—a concept with powerful applications in robot navigation, computer networks, and chemical structure analysis. Alrowaili has made significant contributions to the study of fault-tolerant resolvability, introducing new frameworks for understanding how networks can maintain navigational accuracy even when nodes or edges fail. Her 2022 paper on fault-tolerant resolvability in subdivision graphs (6 citations) has become a foundational reference for researchers designing resilient sensor networks and indoor positioning systems. She has also advanced the theory of edge metric dimension, applying it to Toeplitz networks—interconnection architectures prized for their small diameter, symmetry, and reliability. Her 2021 work on this topic (4 citations) demonstrates how these networks can be optimized for intelligent routing and autonomous navigation. Alrowaili’s research bridges abstract graph invariants with real-world engineering challenges, making her work essential reading for anyone interested in the mathematical foundations of network resilience, machine learning, and robotics. Her publications continue to shape how researchers model and solve problems in distributed systems and chemical informatics.