Jayakrishnan Unnikrishnan

General Electric (United States), Qualcomm (United States)

Papers

Total Citations

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About

Jayakrishnan Unnikrishnan 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 he investigates how to solve linear systems when the order of measurements is unknown or permuted. This foundational work, particularly his 2015 paper "Unlabeled sensing: Solving a linear system with unordered measurements" (35 citations), and its follow-up "Unlabeled Sensing With Random Linear Measurements" (10 citations), has opened new avenues for understanding data association in distributed sensing, privacy, and communication systems. By focusing on random measurement matrices, Unnikrishnan has provided critical theoretical insights into the feasibility and algorithms for recovering signals from scrambled data. His research is highly relevant for modern applications where data arrives out of order or is deliberately anonymized. Through these contributions, Unnikrishnan has established himself as a key voice in the emerging field of unlabeled sensing, offering elegant solutions to a problem that challenges conventional linear algebra and statistical inference.