Zhibin Liu | Computer Science and Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr. Zhibin Liu | Computer Science and Artificial Intelligence | Best Researcher Award

Associate professor | School of Computer Science Qufu Normal University | China

Assoc. Prof. Dr. Zhibin Liu is a highly regarded academic and researcher in the field of computer science, currently serving as an associate professor at the School of Computer Science, Qufu Normal University. His professional journey demonstrates a strong commitment to advancing innovation in computing technologies, particularly within the areas of Internet of Things (IoT), machine learning, and reinforcement learning. He has established himself as a recognized scholar with notable research outputs and impactful contributions to the development of intelligent systems. His academic career reflects a balance between teaching excellence, high-quality research, and meaningful international collaborations, making him a respected figure among peers and students alike.

Professional Profile 

Education

Assoc. Prof. Dr. Zhibin Liu completed his doctoral studies in computer application technology at Hohai University, where he developed expertise in advanced computing techniques and intelligent network optimization. Prior to this, he obtained his master’s degree in computer science from Xi’an University of Science and Technology. His educational background has laid the foundation for his strong analytical skills, deep technical knowledge, and a research-oriented mindset that supports the integration of theory with practical applications. These academic milestones highlight his determination to pursue excellence in the evolving field of computer science and to contribute to the broader scholarly community through innovation and thought leadership.

Experience

Assoc. Prof. Dr. Zhibin Liu has extensive teaching and research experience in the domains of IoT, wireless communication systems, and computational intelligence. At Qufu Normal University, he has played a crucial role in mentoring students, supervising research projects, and leading collaborative academic initiatives. His contributions extend beyond national boundaries, as he has engaged with international researchers to develop joint solutions for pressing technological challenges. He has been actively involved in projects that emphasize routing algorithms, optimization of resource allocation, and the integration of reinforcement learning into real-world applications. Through his sustained efforts, he has strengthened the research culture within his institution and contributed to the growth of knowledge in cutting-edge areas of computer science.

Research Interest

The research interests of Assoc. Prof. Dr. Zhibin Liu are diverse and aligned with the global challenges in modern computing systems. His primary focus lies in the research and application of IoT and machine learning, with specific emphasis on routing algorithms and efficient resource allocation strategies for wireless sensor networks. He also investigates the integration of deep reinforcement learning to address optimization problems within communication systems. His work reflects a clear vision of enhancing scalability, energy efficiency, and computational performance in intelligent systems. This forward-looking research agenda demonstrates his commitment to bridging the gap between theoretical advancements and practical applications, ensuring his contributions remain relevant in addressing real-world challenges.

Award

Assoc. Prof. Dr. Zhibin Liu has received recognition for his impactful contributions to research and academia. His awards and honors highlight his role as a leading researcher in IoT and machine learning, particularly for his pioneering work in reinforcement learning and network optimization. These accolades reflect both institutional and scholarly recognition, positioning him as an influential figure in his domain. His dedication to academic excellence, research innovation, and community engagement has earned him respect and acknowledgment at both national and international levels. Such achievements signify his outstanding professional standing and underscore why he is a strong candidate for this award.

Selected Publication

  • Reinforcement learning based on multi agent value distribution for beamforming optimization in cellular networks (Published: 2021, Citations: 45).

  • Optimization of computational efficiency in IoT based on joint assistance of ARIS and UAV in MEC systems (Published: 2022, Citations: 32).

  • Energy efficient resource allocation strategy for wireless sensor networks using reinforcement learning (Published: 2020, Citations: 56).

  • Dynamic routing algorithm for IoT enabled smart environments through machine learning optimization (Published: 2019, Citations: 61).

Conclusion

Assoc. Prof. Dr. Zhibin Liu exemplifies academic excellence, research innovation, and leadership in the field of computer science. His contributions to IoT systems, reinforcement learning, and wireless network optimization have had significant influence, both in advancing knowledge and in enabling applications that address complex challenges in communication and computing. His achievements in research, combined with his dedication to mentoring students and leading collaborations, reflect his strong leadership qualities. With his consistent record of high-quality publications, international collaborations, and commitment to innovation, Assoc. Prof. Dr. Zhibin Liu is an outstanding candidate for this award. His future endeavors hold immense potential to shape the evolution of intelligent systems and contribute meaningfully to the global academic and scientific community.

Yue Wu | Machine Learning | Best Researcher Award

Yue Wu | Machine Learning | Best Researcher Award

Assist. Prof. Dr Yue Wu, Hangzhou Dian, China

Assist. Prof. Dr. Yue Wu is a promising young academician whose work bridges the gap between automation, machine learning, and electronic design automation. Currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, China, he exemplifies research excellence through his interdisciplinary expertise. He earned his Ph.D. from Zhejiang University in Aeronautics and Astronautics and a B.S. from Wuhan University of Technology in Automation. His scholarly interests focus on logic synthesis, physical design, and intelligent prediction algorithms using graph neural networks. Despite his early career stage, Dr. Wu has demonstrated impactful contributions to both academia and industry-relevant applications. His recent publication on pre-routing slack prediction using graph attention networks stands out as a novel solution in the realm of EDA. With a strong academic foundation and active research output, Dr. Wu is a fitting nominee for the Best Researcher Award, representing the next generation of innovation in AI-driven engineering.

Publication Profile

Orcid

Education

Dr. Yue Wu has a solid educational foundation in engineering and automation. He earned his Bachelor of Science (B.S.) in Automation from the Wuhan University of Technology in 2018. There, he developed a robust understanding of control systems, signal processing, and computational modeling. Pursuing his academic passion, he undertook doctoral studies at the School of Aeronautics and Astronautics, Zhejiang University, one of China’s premier research institutions. He completed his Ph.D. in 2023, focusing on interdisciplinary topics combining aeronautical engineering, data science, and intelligent systems. His doctoral work incorporated advanced machine learning techniques and their applications in hardware-aware environments, preparing him to lead novel research at the intersection of automation and electronics. This strong academic background equips him with the theoretical depth and practical experience essential for future-forward research in intelligent systems and electronic design automation.

Experience

Dr. Yue Wu is currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, since 2023. Despite being in the early phase of his academic career, he has demonstrated exceptional scholarly promise through teaching, mentorship, and high-impact research. His role involves designing and delivering advanced courses on machine learning, logic circuits, and digital system design while actively supervising undergraduate and graduate research projects. He collaborates with interdisciplinary teams, focusing on the integration of machine learning techniques into physical design and logic synthesis processes, bridging hardware and AI innovations. Prior to this, he was involved in multiple research projects at Zhejiang University during his Ph.D., contributing to algorithm development and experimental validation of graph-based learning techniques. Dr. Wu’s combined expertise in automation, EDA tools, and machine learning positions him as a rising leader in academic research and technological advancement.

Awards and Honors

As a rising scholar, Dr. Yue Wu has been recognized for his academic achievements and research contributions. While specific institutional or national awards are yet to be recorded in the public domain, his selection as a faculty member at Hangzhou Dianzi University, known for its emphasis on electronic and information technology research, is a testament to his academic caliber. His recent first-author publication in the peer-reviewed journal “Automation” (2025) highlights his research excellence and innovation in the application of graph attention networks to pre-routing slack prediction, a complex problem in VLSI design. Additionally, his collaborative projects during his Ph.D. at Zhejiang University received internal recognition and contributed to multiple research grants. Dr. Wu’s research profile is steadily growing, and he is well on the path toward future accolades at the national and international levels as he continues to publish and lead in cutting-edge interdisciplinary domains.

Research Focus

Dr. Yue Wu’s research focuses on the intersection of machine learning and electronic design automation (EDA). His primary interest lies in developing intelligent systems that enhance the physical design and logic synthesis processes used in integrated circuit (IC) design. By leveraging advanced models like graph neural networks (GNNs) and attention-based architectures, Dr. Wu aims to address critical challenges such as slack prediction, timing analysis, and routing optimization. His expertise also extends to hardware-aware machine learning, wherein algorithmic efficiency is optimized for real-world applications in chip manufacturing. His recent work—“Pre-Routing Slack Prediction Based on Graph Attention Network”—demonstrates his ability to combine theoretical AI models with practical EDA problems. By pushing the boundaries of design automation through AI integration, Dr. Wu contributes to faster, smarter, and more power-efficient chip design—critical for the next generation of computing devices. His vision is to make intelligent design automation a core component of future electronics engineering.

Publication Top Notes