Saki Nomura

Tokyo University of Agriculture and Technology

Papers

Total Citations

H-Index

About

Saki Nomura is a rising researcher in the field of graph signal processing and sensor network optimization. Her primary research focuses on developing mathematical frameworks for efficient data acquisition and reconstruction from networked systems. Nomura's most significant contribution is her pioneering work on dynamic sensor placement, where she extends classical sampling theory to time-varying graph signals. In her highly cited 2024 paper, she addresses the challenge of selecting optimal sensor locations when sensors can physically move within a network over time, a problem critical for applications in environmental monitoring, smart grids, and mobile robotics. This work bridges the gap between static sensor placement strategies and real-world systems where mobility is a key advantage. While her citation count is still growing, her research has already garnered attention for its novel theoretical approach to a practical engineering problem. Nomura's contributions are particularly valuable for students and researchers working at the intersection of signal processing and network science, offering a rigorous mathematical foundation for designing adaptive sensing systems that can respond to changing network conditions.