Mahmoud Sajjadi is a motivated Ph.D. student in Computer Science and Engineering at the University of Nevada, Reno, with extensive experience in machine learning, electrical and control engineering, and data center design and management. He has a proven track record of success in research, teaching, and industry roles, applying privacy-preserving distributed systems, federated learning, and optimization to solve power system challenges. Mahmoud has a strong foundation in mathematics and proficiency in various programming languages and tools.
Author Metrics
ORCID Profile
Mahmoud Sajjadi has contributed to several high-impact research papers and conferences, with publications under review in notable journals and conferences. His work has been accepted at prestigious venues such as the IEEE International Conference on Distributed Computing Systems (ICDCS) and the IEEE PES General Meeting. His contributions have an acceptance rate of around 21.9% in highly competitive conferences, reflecting the quality and significance of his research.
Education
Mahmoud holds a Ph.D. in Computer Science and Engineering from the University of Nevada, Reno, expected to be completed in May 2025. He earned his M.Sc. in Electrical Engineering-Control Systems from the University of Tehran, Iran, in 2014, where he focused on simultaneous state estimation and reinforcement learning in stochastic matrix games. He also holds a B.Sc. in Electrical Engineering-Control Systems from Shiraz University of Technology, Iran, obtained in 2011.
Research Focus
Mahmoud’s research focuses on privacy-preserving machine learning, federated learning, and optimization, particularly in power systems. He has developed innovative algorithms and models to enhance the efficiency and security of distributed learning systems. His work includes designing and implementing tree-based and neural network-based event classifiers, improving federated learning privacy, and optimizing machine learning algorithms for heterogeneous clients.
Professional Journey
Mahmoud’s professional journey includes significant roles in academia and industry. As a Graduate Research Assistant at the University of Nevada, Reno, he has made substantial contributions to developing privacy-preserving ML algorithms and distributed learning techniques. Prior to this, he served as a Data Center Designer and Manager at Prochista (Sematec, MCI) in Tehran, Iran, where he led AI model development, data center infrastructure management, and cybersecurity initiatives. He also worked as a Power Specialist Engineer at Arya Heavy Machinery (Caterpillar) in Tehran, guiding technical aspects of generator sales and market analysis.
Honors & Awards
Mahmoud has received several honors and awards, including the GSA Outstanding Graduate Researcher Award from the University of Nevada, Reno, in 2024. He was awarded the TechWise program scholarship by TalentSprint Inc., supported by Google, for the period 2023-2024. He is a member of the National Organization for Development of Exceptional Talents of Iran and has earned multiple certifications in data center design and management.
Publications Noted & Contributions
Mahmoud has numerous publications to his credit, including works under review and accepted papers in prestigious journals and conferences. His notable publications include “Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach,” “Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning,” and “Generative Artificial Intelligence for Distributed Learning to Enhance Smart Grid Communication.” His research contributions span across federated learning, distributed learning, and smart grid communication.
- Title: Generative Artificial Intelligence for Distributed Learning to Enhance Smart Grid Communication
- Journal: International Journal of Intelligent Networks
- Year: 2024
- DOI: 10.1016/j.ijin.2024.05.007
- Contributors:
- Seyed Mahmoud Sajjadi Mohammadabadi
- Mahmoudreza Entezami
- Aidin Karimi Moghaddam
- Mansour Orangian
- Shayan Nejadshamsi
Summary: The article likely explores the application of generative artificial intelligence (AI) techniques for distributed learning in improving communication within smart grid systems. Smart grids are modern electrical grids that incorporate digital communication technology to monitor and manage electricity supply more efficiently. Enhancing communication within smart grids is crucial for ensuring reliability, efficiency, and resilience in power distribution. By leveraging generative AI, the authors may propose innovative methods to optimize communication protocols, data transmission, or network management within smart grid infrastructures. This research could contribute to advancing the development and implementation of intelligent systems for future energy networks.
Research Timeline
Mahmoud’s research timeline is marked by continuous contributions from 2014 to the present. Starting with his work on simultaneous learning and state estimation during his M.Sc., he has progressively advanced to developing cutting-edge federated learning algorithms and models for power systems and smart grids. His ongoing projects and research efforts reflect his dedication to advancing the field of distributed learning and machine learning applications.
Collaborations and Projects
Throughout his career, Mahmoud has collaborated with esteemed researchers and institutions. At the University of Nevada, Reno, he worked with Dr. L. Yang, F. Yan, and J. Zhang on federated learning and decentralized multi-agent learning projects. His industry collaborations at Prochista involved leading R&D teams and supervising data center construction projects. Mahmoud has also been involved in cross-disciplinary research experiences for undergraduates on big data analytics in smart cities, funded by NSF.