Dr. Amir Hossein Poursaeed | Engineering | Best Researcher Award

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Dr. Amir Hossein Poursaeed | Engineering | Best Researcher Award

Phd Candidate at University of Exeter, United Kingdom

Amir Hossein Poursaeed is an accomplished researcher in electrical engineering with a specialization in power systems, machine learning applications, and renewable energy integration. Holding a Master’s degree from Lorestan University, he has developed a strong academic foundation complemented by an exceptional research portfolio. His work focuses on power system protection, stability, and optimization using advanced AI techniques such as explainable deep learning and quantum neural networks. With over 17 peer-reviewed journal publications, many in Q1 journals, and multiple IEEE conference contributions, his research demonstrates both depth and innovation. He collaborates with leading academics internationally and has contributed to interdisciplinary studies in environmental modeling and water resource management. Amir’s commitment to cutting-edge research in inverter-based power grids, fault diagnosis, and energy systems places him among the promising young scholars in the field. His achievements reflect a rare blend of technical expertise, research leadership, and forward-looking vision essential for shaping the future of smart grids.

Professional Profile 

Google Scholar
ORCID Profile 

Education

Amir Hossein Poursaeed has a solid educational background in electrical engineering with a focus on power systems. He earned his Master of Science degree from Lorestan University, Iran, where he specialized in Digital Power System Protection and Power System Dynamics. His M.Sc. thesis, supervised by Professor Farhad Namdari, focused on using Support Vector Machines for wide-area protection against voltage and transient instabilities. He previously obtained his Bachelor of Science in Electrical Engineering from the same university, where he explored the optimal placement of phasor measurement units using metaheuristic algorithms. His academic performance was commendable, with a GPA of 18.87/20 in his M.Sc. program, demonstrating both technical strength and research capability. Throughout his education, he consistently focused on high-voltage systems, optimization, and smart grid technologies, laying the foundation for his research in AI-based power system protection and stability. His educational journey highlights a continuous commitment to excellence and innovation in energy systems.

Professional Experience

Amir Hossein Poursaeed has developed a robust professional profile centered around advanced power system research and academic collaboration. While specific institutional roles aren’t explicitly mentioned, his extensive list of high-impact publications indicates active involvement in collaborative research projects, particularly with institutions such as Lorestan University and international partners. He has co-authored multiple studies with recognized scholars, including Professor Farhad Namdari and Dr. P.A. Crossley, highlighting his integration into the global research community. His contributions include the design of advanced fault detection systems, AI-driven stability analysis tools, and renewable energy integration models. Additionally, his work in inter-turn fault diagnosis and real-time system protection showcases applied engineering skills with a focus on practical solutions for modern grid challenges. His experience spans theoretical research, model development, and algorithm implementation in live or simulated systems, establishing him as a well-rounded researcher in academia and an emerging leader in AI-enabled power engineering technologies.

Research Interest

Amir Hossein Poursaeed’s research interests are rooted in the intersection of electrical power systems and artificial intelligence. His primary focus includes power system stability, digital protection systems, fault detection, and the integration of renewable energy sources. He is especially passionate about leveraging advanced machine learning and explainable AI techniques for enhancing grid reliability and system monitoring. His recent work involves deep learning, support vector machines, and quantum neural networks applied to inverter-based power systems and DC microgrids—fields gaining global relevance due to the rise of decentralized energy systems. Optimization algorithms, transient analysis, and wide-area protection schemes are other key domains of his expertise. He also extends his knowledge into environmental systems, working on AI-based models for water quality assessment. This multidisciplinary approach underlines his goal of developing intelligent, robust, and real-time frameworks for smart grid operations, making his research both innovative and impactful in addressing contemporary and future challenges in energy systems.

Award and Honor

Although specific awards and honors are not listed, Amir Hossein Poursaeed’s academic and research accomplishments position him as a candidate deserving of high recognition. His publication record in prestigious Q1 journals, such as Applied Soft Computing, Energy Reports, and Sustainable Energy Technologies and Assessments, reflects scholarly excellence. His papers have introduced novel contributions to power system protection and AI-based monitoring, often co-authored with leading international experts—an indication of his growing reputation in the field. His research has also been accepted at major IEEE conferences, including the International Universities Power Engineering Conference and the International Conference on Electric Power and Energy Conversion Systems, which highlights peer recognition of his work. Moreover, his interdisciplinary research in water resource management using machine learning models demonstrates his versatility and impact beyond core power engineering. Given these achievements, he is highly deserving of academic awards, particularly those that celebrate emerging researchers and innovators in smart energy systems.

Conclusion

Amir Hossein Poursaeed is an emerging thought leader in the field of power systems and intelligent energy technologies. With a strong educational background and a research focus on AI-driven solutions for grid stability and protection, he has consistently demonstrated excellence in both theoretical innovation and practical application. His contributions span power engineering, machine learning, and even environmental sciences—showcasing his ability to bridge disciplines for impactful solutions. Through numerous high-impact publications and international conference engagements, he has established himself as a respected voice in the global research community. His work addresses critical challenges in inverter-based grids, renewable integration, and real-time monitoring, aligning perfectly with the global shift toward sustainable and resilient energy systems. Amir’s trajectory reflects not only technical brilliance but also research leadership, collaboration, and a vision for smarter, safer, and more efficient power systems. He is undoubtedly a strong candidate for honors such as the Best Researcher Award.

Publications Top Notes

  • Title: An Ultra-Fast Directional Protection Scheme for DC Microgrids Based on High-Order Synchrosqueezing Transform
    Authors: A.H. Poursaeed, F. Namdari
    Year: 2023
    Citations: 7

  • Title: Online Transient Stability Assessment Implementing the Weighted Least-Square Support Vector Machine with the Consideration of Protection Relays
    Authors: A.H. Poursaeed, F. Namdari
    Year: 2025
    Citations: 6

  • Title: A New Strategy for Prediction of Water Qualitative and Quantitative Parameters by Deep Learning-Based Models with Determination of Modelling Uncertainties
    Authors: M. Poursaeid, A.H. Poursaeed
    Year: 2024
    Citations: 6

  • Title: Online Voltage Stability Monitoring and Prediction by Using Support Vector Machine Considering Overcurrent Protection for Transmission Lines
    Authors: A.H. Poursaeed, F. Namdari
    Year: 2020
    Citations: 6

  • Title: High‐Speed Algorithm for Fault Detection and Location in DC Microgrids Based on a Novel Time–Frequency Analysis
    Authors: A.H. Poursaeed, F. Namdari
    Year: 2024
    Citations: 3

  • Title: Hydraulic Modeling of the Water Resources Using Learning Techniques
    Authors: M. Poursaeid, A.H. Poursaeed, S. Shabanlou
    Year: 2022
    Citations: 3

  • Title: Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids
    Authors: A.H. Poursaeed, F. Namdari
    Year: 2025
    Citations: 2

  • Title: Simulation Using Machine Learning and Multiple Linear Regression in Hydraulic Engineering
    Authors: M. Poursaeid, A.H. Poursaeed, S. Shabanlou
    Year: 2023
    Citations: 2

  • Title: Optimized Explainable Tabular Transformer Model for Fault Localization in DC Microgrids
    Authors: A.H. Poursaeed, F. Namdari, P.A. Crossley
    Year: 2025
    Citations: 1

  • Title: Optimal Coordination of Directional Overcurrent Relays: A Fast and Precise Quadratically Constrained Quadratic Programming Solution Methodology
    Authors: A.H. Poursaeed, M. Doostizadeh, S. Hossein Beigi Fard, A.H. Baharvand, F. Namdari
    Year: 2024
    Citations: 1

Xingjia Li | Engineering | Best Researcher Award

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Dr. Xingjia Li | Engineering | Best Researcher Award

Senior Engineer at Shanghai Liangxin Electrical Co. Ltd, China

Dr. Xingjia Li is a promising early-career researcher who earned his Ph.D. in Mechanical Engineering from Jiangsu University in 2023. He is currently a Postdoctoral Associate at the postdoctoral workstation of Shanghai Liangxin Electrical Co., Ltd. in Shanghai, China. His research focuses on robotics and electrical systems, with a particular emphasis on sensor data processing using machine learning techniques. Dr. Li is dedicated to advancing human-centered applications by enhancing the reliability, intelligence, and security of robotic systems in daily life. His interdisciplinary approach integrates mechanical engineering, electronics, and artificial intelligence, aligning with the evolving demands of modern technology. While still in the early stages of his research career, his industry collaboration and applied research focus position him as a strong candidate for future leadership in his field. Dr. Li’s work holds potential for significant contributions to smart systems and intelligent automation in real-world environments.

Professional Profile 

ORCID Profile

Education

Dr. Xingjia Li obtained his Ph.D. in Mechanical Engineering from Jiangsu University, Zhenjiang, China, in 2023. During his doctoral studies, he focused on the integration of robotics and intelligent systems, building a strong foundation in both theoretical and applied aspects of mechanical and electrical engineering. His education emphasized sensor systems, automation, and machine learning, which prepared him for interdisciplinary research and practical implementation in advanced robotics. Dr. Li demonstrated strong academic performance and research capabilities throughout his graduate studies, contributing to academic discussions and research forums. His educational background reflects a rigorous training in engineering principles, analytical thinking, and innovation, which has shaped his approach to problem-solving in complex systems. Through research projects, seminars, and collaboration with faculty, he developed a deep understanding of how mechanical systems can be enhanced through intelligent control and data-driven methods, laying the groundwork for his postdoctoral research and future contributions to intelligent automation.

Professional Experience

Following his Ph.D., Dr. Xingjia Li joined the postdoctoral workstation at Shanghai Liangxin Electrical Co., Ltd., a key player in the electrical technology industry. In this role, he has been actively involved in research and development, focusing on advanced robotics and intelligent systems. His work emphasizes real-world implementation of sensor-based machine learning techniques to enhance system performance, reliability, and human-machine interaction. At Liangxin, Dr. Li collaborates with both engineering teams and academic partners to design and improve intelligent robotic systems that can operate efficiently in complex environments. His professional experience bridges academia and industry, allowing him to apply theoretical models to practical challenges in automation and electrical systems. This hands-on engagement with cutting-edge technologies has not only expanded his technical skill set but also positioned him as a valuable contributor in the emerging fields of smart manufacturing and AI-powered industrial automation, where reliability and adaptive performance are critical.

Research Interest

Dr. Xingjia Li’s research interests lie at the intersection of robotics, electrical systems, and machine learning, with a strong focus on sensor data processing for human-centered applications. He is passionate about enhancing the intelligence, reliability, and safety of robotic systems operating in dynamic environments. His work aims to empower robots with the ability to interpret complex sensory inputs through machine learning algorithms, thereby enabling real-time decision-making and adaptive behavior. He is particularly interested in applications that improve quality of life, such as assistive robotics, industrial automation, and intelligent monitoring systems. By integrating advanced data analytics and control strategies, Dr. Li seeks to develop systems that can function autonomously with minimal human intervention while maintaining high levels of trust and safety. His interdisciplinary approach combines the strengths of mechanical design, signal processing, and artificial intelligence, positioning him to contribute meaningfully to the advancement of next-generation robotics and smart systems.

Award and Honor

As a rising researcher in the field of intelligent robotics, Dr. Xingjia Li is at the beginning of his professional recognition journey. While specific awards and honors have not been listed in the available information, his acceptance into a postdoctoral research position at Shanghai Liangxin Electrical Co., Ltd. itself signifies recognition of his academic potential and technical proficiency. The opportunity to work in a dedicated industrial research environment reflects a high level of trust in his expertise and capability to contribute to meaningful innovation. His early involvement in cutting-edge projects and interdisciplinary work also positions him as a strong candidate for future academic and industrial awards. As he continues to publish research, develop prototypes, and contribute to real-world solutions, it is expected that Dr. Li will accumulate professional honors that recognize his growing impact in the fields of robotics, electrical systems, and intelligent automation technologies.

Conclusion

Dr. Xingjia Li is an emerging researcher whose interdisciplinary expertise bridges mechanical engineering, robotics, and artificial intelligence. With a strong educational foundation from Jiangsu University and practical postdoctoral experience at Shanghai Liangxin Electrical Co., Ltd., he is well-positioned to make significant contributions to the field of intelligent systems. His research aims to improve human-robot interaction and automation reliability through advanced sensor data processing and machine learning techniques. Though still in the early stages of his career, Dr. Li’s work shows great promise for practical impact in industry and society. His commitment to innovation, real-world application, and cross-disciplinary collaboration sets the stage for a distinguished research trajectory. With continued focus, publication, and recognition, Dr. Li has the potential to emerge as a thought leader in the development of smart, adaptive, and secure robotic systems that support both industrial and human-centered needs.

Publications Top Notes