Alireza Aghasi

Oregon State University, Georgia State University

Papers

Total Citations

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About

Alireza Aghasi is a researcher advancing the frontiers of distributed optimization and multi-agent systems, with a focus on dynamic networks and resource-constrained environments. His work addresses critical challenges in coordinating autonomous agents—such as mobile robots or smart grid nodes—over time-varying communication topologies. In his highly cited 2024 paper (30 citations), Aghasi introduced a discrete-time distributed optimization algorithm for dynamic directed graphs, enabling robust consensus and learning in mobile multi-agent systems under switching network structures. This work bridges continuous-time theory with practical discrete-time implementations, a key step for real-world applications like distributed machine learning. His 2022 contribution tackles finite-sum constrained optimization, developing localized allocation techniques for multi-agent networks with nonlinear node dynamics—relevant to parallel data processing and smart infrastructure. While early in his career, Aghasi’s research demonstrates a clear trajectory toward scalable, resilient algorithms for decentralized control and optimization. His contributions are particularly valuable for students and researchers working at the intersection of control theory, networked systems, and distributed AI, offering foundational tools for next-generation autonomous networks.