Prof. Jun Li | Mobile Networks | Best Researcher Award

Prof. Jun Li, Southeast University, China

Jun Li is a distinguished professor at Southeast University’s School of Information Science and Engineering. A prolific researcher in engineering with a career spanning over two decades, he has contributed significantly to advancements in Industrial Internet, 6G communications, and data privacy in federated learning. Jun Li’s pioneering work has been cited over 15,000 times, showcasing his profound impact on these fields. He previously served as a professor at Nanjing University of Science and Technology and held research positions at esteemed institutions such as Princeton University and the University of Sydney. His accomplishments include the Youth Thousand Talents Program and AI Digital Craftsman Award. As an active member of China’s technical communities, Jun Li is highly regarded for his innovative research, esteemed publications, and ongoing contributions to the development of intelligent systems and industrial data solutions.

Professional Profile

Google Scholar

suitability summary of best reseacher award

Jun Li stands as a highly qualified candidate for the Best Researcher Award, demonstrating a blend of extensive research contributions, leadership, and technological advancements across domains like industrial internet, wireless communications, and artificial intelligence. As a professor in the School of Information Science and Engineering at Southeast University, his influence extends through both his innovative research and his commitment to developing critical solutions in fields that are vital to technological advancement and industrial applications.

Education 

Jun Li holds a Ph.D. in Engineering from Shanghai Jiao Tong University (2009), where he specialized in advanced wireless communication systems. Prior to this, he earned his M.S. in Engineering from Nanjing University of Science and Technology (2004), focusing on foundational engineering principles that later influenced his work in Industrial Internet and data privacy. His academic journey began with a B.S. in Engineering from Jilin University (2001), providing a strong theoretical base in engineering. This diverse educational background across three premier Chinese universities has shaped Jun Li’s expertise in systems integration and collaborative cloud-edge systems for industrial applications. His academic foundations underpin his innovative approaches to developing new methods in 6G wireless technologies, privacy-preserving systems, and industrial IoT, which continue to influence his teaching and research at Southeast University and beyond.

Experience

Currently a professor at Southeast University’s School of Information Science and Engineering (2024–present), Jun Li has previously held a series of esteemed positions. He was a professor at Nanjing University of Science and Technology (2015–2024), where he advanced research in Industrial Internet and next-generation communication systems. As a visiting scholar at Princeton University (2018–2019), he collaborated under Vincent Poor, exploring innovative data privacy techniques in federated learning. Earlier, he was a research fellow at the University of Sydney (2012–2015), where he contributed to projects under Branka Vucetic on cloud-based systems for industrial IoT applications. His postdoctoral research at the University of New South Wales (2009–2012) further deepened his expertise in wireless communication. With a brief role at Shanghai Bell R&D Center, Jun Li’s diverse experience positions him as a leader in both industrial applications and academic research, emphasizing the practical and scalable development of advanced technological systems.

Awards and Honors 

Jun Li’s career is marked by numerous prestigious awards, including the Youth Thousand Talents Program by the Ministry of Organization (2016) and the Distinguished Professor title from Jiangsu Province (2015). His innovative contributions have also earned him the Excellent Scientist Award from the Chinese Institute of Electronics (2022) and the Global Top 2% Scientist recognition (2023). In the AI field, he received the AI Digital Craftsman Award from the Chinese Association for Artificial Intelligence (2023). His academic contributions have garnered several competitive awards, including multiple Best Paper Awards from IEEE conferences and a top distinction from the National Industrial Internet Innovation Competition (2023). Jun Li’s accolades reflect his prominent role in advancing digital innovation and underscore his commitment to high-impact, interdisciplinary research that addresses key challenges in engineering and industrial communication technologies.

Research Focus

Jun Li’s research is centered on integrating advanced wireless communication with the Industrial Internet, focusing on 6G and privacy-preserving technologies for data management. His recent projects address the development of Intelligent Collaborative Cloud-Edge Systems tailored for industrial applications, aiming to enhance real-time data processing and decision-making in large-scale IoT networks. His work in federated learning emphasizes privacy protection, particularly in sensitive industrial settings, where secure data flow and efficient resource allocation are paramount. Jun Li is also pioneering efforts in positioning systems for asset management, optimizing data flow, and ensuring robust privacy protocols in wireless networks. His research interests extend to high-performance computing solutions for industrial data and cutting-edge strategies in collaborative AI applications, reflecting a comprehensive approach to advancing the industrial IoT ecosystem through integrative, secure, and intelligent technologies.

 Publication Top Notes

• Federated Learning with Differential Privacy: Algorithms and Performance Analysis

  • Cited by: 1801

• Federated Learning for Internet of Things: A Comprehensive Survey

  • Cited by: 927

• Simultaneous Wireless Information and Power Transfer (SWIPT): Recent Advances and Future Challenges

  • Cited by: 899

• 6G Internet of Things: A Comprehensive Survey

  • Cited by: 852

• Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

  • Cited by: 504