Seyedmohammad Azimi Abarghouei is a Senior Researcher at the Division of Network and Systems Engineering, KTH Royal Institute of Technology, collaborating with Prof. Carlo Fischione. Previously, he served as a Postdoctoral Fellow in the same division from 2022 to 2024 under Prof. Viktoria Fodor. He completed his Ph.D. at Sharif University of Technology in Iran in November 2020, where his research on wireless networks earned the national Best PhD Thesis Award in electrical engineering and information technology. His educational background includes: Ph.D. in Electrical Engineering and Information Technology, Sharif University of Technology, Iran (2020) Dr. Azimi Abarghouei's research centers on distributed machine learning systems, particularly federated learning architectures for wireless networks. His work develops hierarchical aggregation methods, over-the-air computation techniques, and lattice coding approaches to address communication efficiency and data heterogeneity. He also contributes to molecular communication and stochastic geometry for nanonetwork modeling, bridging theoretical foundations with practical wireless applications. Analysis of his recent publications reveals a dominant focus on scalable federated learning systems, with significant innovation in hierarchical structures, quantization techniques, and interference-aware aggregation. His work consistently targets real-world challenges in edge intelligence, including bandwidth constraints, non-IID data distributions, and massive device coordination in IoT environments. His scientific recognition includes: Best PhD Thesis Award in Electrical Engineering and Information Technology (National Level, Iran) Jubilee Appropriation Scholarship from Knut and Alice Wallenberg Foundation (2023) Exemplary Reviewer of IEEE Wireless Communications Letters (2022, 2023, 2025) Exemplary Reviewer of IEEE Communications Letters (2023) Dr. Azimi Abarghouei has secured competitive research funding including the Knut and Alice Wallenberg Foundation's Jubilee Appropriation Scholarship. His active peer review contributions and conference tutorial leadership (e.g., IEEE ICMLCN 2025) demonstrate significant community engagement. While specific grant details beyond the scholarship aren't fully documented, his publication record indicates sustained support for federated learning research through institutional and foundation channels. He operates within Prof. Carlo Fischione's research group at KTH's Division of Network and Systems Engineering, focusing on networked machine learning systems. His collaborative work spans international conferences and journals, with recent emphasis on federated learning standardization through tutorials and preprints that shape emerging research directions in distributed AI.
