I’m Mahmoud Sajjadi, a PhD student in Computer Science and Engineering at the University of Nevada, Reno. I am passionate about solving complex problems and extracting valuable insights from data. I am proficient in programming languages such as Python, MATLAB, and more, and am able to develop custom algorithms, models, and applications to meet specific needs.
My current research focuses on developing machine learning algorithms in distributed systems and improving privacy in machine learning methods. Also I am interested in using machine learning to improve the efficiency and reliability of power grids, and to protect the privacy of individuals' data.
As someone with a background in machine learning and power systems, I have expertise in the intersection of these two fields. Specifically, I have experience in developing machine learning models for predicting power system behavior and optimizing power system operations. I am familiar with a variety of techniques, including deep learning, reinforcement learning, and decision trees, and have applied them to problems such as load forecasting, fault diagnosis, and energy management. Additionally, I have knowledge of power system design, operation, and control, including topics such as power generation, transmission, distribution, and renewable energy integration. Through my background in machine learning and power systems, I am committed to advancing the field of sustainable energy and optimizing power system performance.
I am always looking for new opportunities to grow and learn. If you have any questions or would like to discuss potential collaboration, please feel free to connect with me on my Linkedin 
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S. M. Sajjadi Mohammadabadi, L. Yang, F. Yan, and J. Zhang, “Communication-efficient training workload balancing for decentralized multi-agent learning,” in IEEE ICDCS 2024. link
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A. Ghasemkhani, R. Sanjeev Haridas, S. M. Sajjadi Mohammadabadi, and L. Yang, “Feature collusion attack on PMU data-driven event classification,” in 2024 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington DC, USA, Feb. 2024. link
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S. M. Sajjadi Mohammadabadi, S. Zawad, F. Yan, and L. Yang, “Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach,” Under review. link
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S. M. Sajjadi Mohammadabadi, Y. Liu, A. Canafe, and L. Yang, “Towards distributed learning of PMU data: A federated learning based event classification approach,” in 2023 IEEE PES General Meeting, Orlando, FL, USA, July, 2023. link