Saeid Haghighatshoar

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About

Saeid Haghighatshoar is a researcher whose work lies at the intersection of signal processing, information theory, and machine learning. His most significant contributions center on the challenging problem of "unlabeled sensing," where the goal is to solve a linear system when the measurement order is unknown or permuted. In his highly cited 2015 paper, "Unlabeled Sensing: Solving a linear system with unordered measurements," he laid the theoretical groundwork for this problem, demonstrating that recovery is possible even without knowing which measurement corresponds to which equation. This work, with 35 citations, has opened new avenues for applications in sensor networks, data association, and privacy. He further extended this analysis in his 2018 follow-up, "Unlabeled Sensing With Random Linear Measurements," which has garnered 10 additional citations. By tackling this fundamental ambiguity, Haghighatshoar has provided critical insights into the limits and capabilities of linear systems under uncertainty, offering elegant mathematical solutions that bridge theory and practical implementation. His research continues to inspire new approaches to handling corrupted or unlabeled data in modern sensing systems.