Saghar Hosseini

Papers

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About

Saghar Hosseini’s research centers on distributed optimization and machine learning, with a particular focus on developing efficient algorithms for networked systems. Her major contribution lies in advancing the Alternating Direction Method of Multipliers (ADMM) for online and distributed settings, enabling multiple decision-makers to collaboratively optimize a global objective under linear constraints without central coordination. Her seminal 2014 paper, “Online Distributed ADMM on Networks,” which has garnered 4 citations, laid foundational groundwork for real-time optimization over networks—a critical capability for applications in sensor networks, smart grids, and multi-agent robotics. This work addresses the challenge of minimizing convex cost functions across interconnected agents, offering both theoretical convergence guarantees and practical implementation strategies. Hosseini’s research bridges the gap between theoretical optimization and real-world distributed systems, making her contributions valuable for students and researchers working on decentralized machine learning, control, and signal processing. Her work continues to influence the design of scalable, communication-efficient algorithms for modern networked environments.