Prof. Zhengyong Feng | Artificial Intelligence | Best Researcher Award

China West Normal University | China

Zhengyong Feng, born in Sichuan, China, is a professor at the School of Electronic Information Engineering, China West Normal University, where he has been contributing to teaching and research with a strong academic background spanning physics, communication systems, and computer science, he has authored scholarly articles and engaged in both national and international research projects, including a joint doctoral program in the U.S. at the University at Buffalo under Prof. Chang-Wen Chen; his interdisciplinary expertise bridges deep learning, reinforcement learning, and visual computing, and he is recognized for his innovative contributions to intelligent systems and robotics; over the years, he has built a strong academic profile through consistent research output, mentorship of graduate students, and involvement in collaborative projects; his career reflects a blend of technical depth, cross-cultural academic engagement, and dedication to advancing the field of intelligent computing and information systems.

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Education:

Zhengyong Feng received his B.S. degree in Physics from China West Normal University , his M.E. degree in Communication and Information Systems from the Institute of Electronics, Chinese Academy of Sciences and his Ph.D. degree in Communication and Information Systems from the University of Electronic Science and Technology of China ; during his Ph.D. studies, he was selected for a prestigious joint doctoral program sponsored by the National Natural Science Foundation of China, through which he conducted research at the Department of Computer Science and Engineering, University at Buffalo, the State University of New York,  under the guidance of Prof. Chang-Wen Chen; this international research experience enriched his understanding of computer science, deepened his technical knowledge, and contributed significantly to the development of his doctoral work; his educational journey reflects a consistent pursuit of excellence across multiple disciplines and institutions at both national and global levels.

Experience:

Zhengyong Feng served as a Research Assistant at the School of Physics and Electronic Information, China West Normal University, where he worked on foundational topics in electronics and communication systems; during his Ph.D. studies, he participated in a joint research program with the University at Buffalo, SUNY, where he engaged in advanced research in visual computing and multimedia systems under the supervision of Prof. Chang-Wen Chen; he has held the position of Professor at the School of Electronic Information Engineering, China West Normal University, where he leads research in artificial intelligence and supervises graduate research projects in deep learning and robotics; his professional experience spans more than two decades and includes teaching, research, and international academic collaboration; throughout his career, he has demonstrated expertise in interdisciplinary research and a commitment to technological innovation and academic excellence in communication and information engineering.

Awards and Honors:

Zhengyong Feng has received multiple recognitions throughout his academic and research career, including selection for a competitive joint doctoral program funded by the National Natural Science Foundation of China, which enabled him to conduct international research in the United States; his academic contributions have earned him local and institutional honors for excellence in teaching, research, and publication; he has been involved in several national and provincial research projects, reflecting his role as a principal investigator and subject expert in his field; although specific titles of awards are not detailed, his consistent publication record, leadership in funded research, and recognition as a senior academic at China West Normal University indicate a high level of respect and acknowledgment from the academic community; his contributions to AI, robotics, and intelligent systems have been instrumental in shaping research directions within his institution and broader academic networks, positioning him as a leader in visual computing and machine learning research.

Research Focus:

Zhengyong Feng’s research focuses on the intersection of artificial intelligence, visual computing, and robotics, with a strong emphasis on deep learning models for image understanding, video analysis, and pattern recognition; he is also deeply engaged in the development of intelligent control systems using reinforcement learning techniques to improve decision-making and adaptability in autonomous systems; his work explores real-time perception, human–machine interaction, and robotic vision, aiming to bridge the gap between machine learning theory and practical applications in robotics and automation; recent projects include using neural networks for semantic segmentation and applying reinforcement learning to complex robotic control tasks in dynamic environments; he is interested in scalable and efficient AI models that can be deployed in real-world contexts; his interdisciplinary approach brings together elements from computer vision, control theory, and cognitive science to create more robust and intelligent machines; his research contributes to the advancement of next-generation smart systems and AI-driven automation.

Publication:

Title: ADD-YOLO: A New Model for Object Detection in Aerial Images
Year: 2025
Citations: 1

Title: IBR-SLAM: Visual SLAM Based on Improved BiSeNet with RGB-D Sensor
Year: 2025

Title: YGDD-SLAM: Direct Geometric Constraint SLAM Based on Object Detection and Depth Image Segmentation
Year: 2025

Title: SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection
Year:  2025

Conclusion:

Zhengyong Feng exemplifies the qualities of a dedicated, forward-thinking researcher whose work in visual computing and robotics reflects both academic rigor and practical relevance. His consistent publication record, commitment to interdisciplinary innovation, and leadership in academic mentorship mark him as a strong contender for the Best Researcher Award. With continued growth in international collaboration and research dissemination, he is well-positioned to achieve even greater impact in the global scientific community.