Education & Experience ππ
- π PhD Student, Beijing Institute of Technology
- π Research Expertise: Multi-agent systems, reinforcement learning, and game theory
- π Published 4 SCI-indexed journal papers, including one in a JCR Q1 journal
- π€ Presented at 5 conferences (2 oral presentations at international events)
- π¬ Contributed to 2 major national research projects
- π Over 10 national/provincial awards, including Outstanding Graduate Student 2024
Suitability Summary
Dr. Licheng Sun is a distinguished PhD student at Beijing Institute of Technology, making outstanding contributions in the fields of Β multi-agent systems, reinforcement learning, and game theory π€. His exceptional research output, including four SCI-indexed journal publications and contributions to national-level research projects, showcases his leadership in engineering and artificial intelligence. His groundbreaking studies on adaptive topological networks, AI-driven decision-making, and deep learning applications position him as a highly deserving candidate for the Best Researcher Award π
.
Professional Development π
Dr. Licheng Sun has actively contributed to advancing research in multi-agent systems and reinforcement learning π€. His work includes adaptive grouping dynamic topological spaces and topological networks in Fourier space π‘. Through his publications in high-impact journals, he has significantly influenced the field of AI and game theory π
. Additionally, he has participated in national-level research projects, strengthening his expertise in cutting-edge technologies π¬. His commitment to academic excellence is evident through his active conference engagements, oral presentations, and awards π. His research aims to bridge theoretical advancements with practical applications in AI-driven decision-making β‘.
Research Focus π¬
Dr. Licheng Sun research is centered on multi-agent reinforcement learning, adaptive topological networks, and game theory π€. His innovative work introduces dynamic topological spaces for enhanced agent collaboration, optimizing decision-making processes π§ . Through his highly cited publications, he explores deep reinforcement learning architectures, integrating topological adaptability π. His contributions to national research projects focus on real-world AI applications, such as helmet-wearing detection systems ποΈ. With a strong emphasis on practical AI solutions, his research aims to develop more robust, scalable, and intelligent agent-based systems for industrial and technological advancements β‘.
Awards & Honors π
- π
Outstanding Graduate Student 2024
- π Over 10 national and provincial awards
- π
Honored in prestigious AI and engineering competitions
- π Recipient of top academic and research excellence awards
- π Published in high-impact journals (SCI: 4, EI: 8)
- π Contributor to major national research projects
- π€ Oral presenter at international conferences
- π
Recipient of multiple innovation and technology awards
Publication Top Notes
π Major Publications
1οΈβ£ GDT: Multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space
- π Journal: Information Sciences
- π
Publication Date: February 2025
- π DOI: 10.1016/j.ins.2024.121646
- π₯ Contributors: Licheng Sun, Hongbin Ma, Zhentao Guo
- π Focus: Multi-agent reinforcement learning with adaptive grouping and dynamic topology.
2οΈβ£ Multi-agent reinforcement learning system framework based on topological networks in Fourier space
- π Journal: Applied Soft Computing
- π
Publication Date: March 2025
- π DOI: 10.1016/j.asoc.2025.112986
- π₯ Contributors: Licheng Sun, Ao Ding, Hongbin Ma
- π Focus: A novel multi-agent learning framework utilizing Fourier space topological networks.
3οΈβ£ HWD-YOLO: A New Vision-Based Helmet Wearing Detection Method
- π Journal: Computers, Materials & Continua
- π
Publication Date: 2024
- π DOI: 10.32604/cmc.2024.055115
- π₯ Contributors: Licheng Sun, Heping Li, Liang Wang
- π Focus: AI-driven safety monitoring system for helmet detection using YOLO-based deep learning.
4οΈβ£ CT-MVSNet: Curvature-guided multi-view stereo with transformers
- π Journal: Multimedia Tools and Applications
- π
Publication Date: April 23, 2024
- π DOI: 10.1007/s11042-024-19227-3
- π₯ Contributors: Liang Wang, Licheng Sun, Fuqing Duan
- π Focus: Transformer-based multi-view stereo depth estimation using curvature-guided enhancements.
Conclusion
Dr.Licheng Sunβs exceptional research, high-impact publications, and contributions to national research projects set him apart as an emerging leader in artificial intelligence and multi-agent learning. His ability to bridge theoretical advancements with real-world applications in AI and engineering makes him a perfect candidate for the Best Researcher Award π
. His dedication to advancing reinforcement learning frameworks, AI-driven safety systems, and topological networks reflects his pioneering role in superior engineering research π.