Assoc. Prof. Dr. Qingguo Lü | Computational Methods | Best Researcher Award

Chongqing University | China

Dr. Qingguo Lü is currently an Associate Professor at the College of Computer Science, Chongqing University, China. With a Ph.D. in Computational Intelligence and Information Processing from Southwest University, his academic journey has been marked by excellence. His work primarily focuses on distributed control and optimization in networked systems, especially in areas involving machine learning, cooperative control, and smart grids.

Profile

Scopus

🎓 Early Academic Pursuits

Dr. Lü began his academic journey with a Bachelor’s degree in Measurement Control Technology and Instrument from Anhui University of Technology, before advancing to a Master’s degree in Signal and Information Processing at Southwest University. His early academic years were dedicated to mastering core concepts of computational intelligence and information processing, laying the foundation for his later groundbreaking research.

💼 Professional Endeavors

Throughout his career, Dr. Lü has held significant positions, including being a Research Assistant at the Texas A&M University Science Program, Qatar, where he contributed to the research in networked control systems, distributed computing, and smart grids. Following this, he transitioned to his postdoctoral research at Chongqing University, collaborating with Prof. Shaojiang Deng on topics like cooperative control, distributed optimization, and machine learning. His role as an Associate Professor has enabled him to further deepen his research and lead academic projects.

🔬 Contributions and Research Focus

Dr. Lü’s research is deeply embedded in solving real-world problems using distributed optimization algorithms across networked systems. Notable contributions include the development of asynchronous algorithms for decentralized resource allocation, privacy protection algorithms, and the design of algorithms for economic dispatch in smart grids. His research focus is centered on improving distributed optimization through stochastic algorithms, cooperative control, and networked machine learning.

📚 Academic Cites

Dr. Lü’s research has been extensively cited in major journals, indicating the high impact of his work. For example, his paper in IEEE Transactions on Cybernetics (2021) has garnered attention for its privacy-masking stochastic algorithms, highlighting his role in advancing the field of privacy in decentralized systems. His consistent contributions to top-tier journals underscore his prominence as a thought leader in computational intelligence and information processing.

🛠 Research Skills

Dr. Lü possesses advanced skills in developing decentralized algorithms, with expertise in distributed optimization, privacy protection, and machine learning for networked systems. His ability to design efficient algorithms that are not only theoretically sound but also computationally feasible has enabled the practical deployment of these methods in diverse real-world applications, including energy optimization and economic dispatch in smart grids.

🏫 Teaching Experience

As an Associate Professor, Dr. Lü plays an active role in shaping the next generation of researchers and engineers. His teaching focuses on distributed control systems, networked optimization, and machine learning, ensuring that students are well-versed in the latest techniques and applications of computational intelligence. His involvement in academic mentorship and research supervision is highly regarded, helping foster a collaborative and innovative research environment.

🏆 Legacy and Future Contributions

Dr. Lü’s career is already distinguished by his extensive research publications, patents, and contributions to academic growth. His research continues to shape the development of distributed algorithms for complex networks, offering solutions that are highly relevant in today’s rapidly evolving technological landscape. Looking ahead, he aims to expand his work on energy optimization, privacy protection, and networked control systems to tackle emerging challenges in fields like smart cities and autonomous systems.

Publications Top Notes

• An Efficient Privacy-Preserving Ranked Multi-Keyword Retrieval for Multiple Data Owners in Outsourced Cloud
  Authors: Li, D., Wu, J., Le, J., Liao, X., Xiang, T.
  Journal: IEEE Transactions on Services Computing
  Year: 2024
• Distributed Primal-Dual Proximal Algorithms for Convex Optimization Involving Three Composite Functions
  Authors: Ran, L., Li, H., Hu, J., Li, Z., Chen, G.
  Journal: IEEE Transactions on Control of Network Systems
  Year: 2024
• Distributed Stochastic Learning for Composite Sharing Optimization in Consumer Electronics
  Authors: Lu, Q., Dai, X., Zhang, W., Ezilarasan, M.R.
  Journal: IEEE Transactions on Consumer Electronics
  Year: 2024
• An Epigraph Approach for Distributed Economic Dispatch in General Dynamic Directed Networks
  Authors: Lü, Q., Zhou, Q., Zhang, K., Wang, Z., Li, H.
  Journal: 6th International Conference on Electronic Engineering and Informatics, EEI 2024
  Year: 2024
• Dynamic-Based Privacy Preservation for Distributed Economic Dispatch of Microgrids
  Authors: Cheng, H., Liao, X., Li, H., Lu, Q.
  Journal: IEEE Transactions on Control of Network Systems
  Year: 2024