Mr. Shantao Ping | Computer Vision | Best Researcher Award

Associate Senior Engineer, Qiyuan Lab, China

Shantao Ping is an Associate Senior Engineer at Qiyuan Lab, specializing in computer vision, artificial intelligence, and large-scale model algorithms. With a Master’s degree in Computer Science, Shantao has a proven track record of driving innovation through cutting-edge research and development. He has contributed to over 28 research and industry projects and holds 14 national invention patents. His collaborative project with Baidu, an AI-powered medical question-answering system, significantly enhanced user engagement and earned him the prestigious Baidu Best Engineer Award. Shantao is also an active member of the Chinese Institute of Command and Control, where he continuously advances the frontiers of intelligent simulation, image processing, and natural language processing. His work focuses on solving complex engineering problems and has made substantial contributions to simulation scene construction and few-shot object recognition. Passionate about applied research, Shantao Ping is committed to shaping the future of intelligent computing through practical and scalable solutions.

Publication Profile

ORCID Profile

Education

Shantao Ping holds a Master’s degree in Computer Science from an esteemed institution, equipping him with solid expertise in artificial intelligence, computer vision, and advanced computational algorithms. He also holds the professional qualification of Associate Senior Engineer, recognized by the Ministry of Human Resources and Social Security (MOHRSS), Beijing, China. This designation reflects his deep technical proficiency and leadership in engineering research and development. Throughout his academic and professional training, Shantao focused on bridging theoretical foundations with real-world applications, emphasizing innovation in structured light calibration, simulation modeling, and machine learning-based image processing. His educational journey laid the groundwork for his current role as a highly effective engineer, capable of contributing to both research excellence and industrial breakthroughs. Shantao’s education emphasizes interdisciplinary collaboration, practical application, and a research-driven approach that aligns perfectly with his long-standing commitment to technological advancement and cutting-edge innovation in the rapidly evolving fields of AI and computer vision.

Experience

Shantao Ping is currently an Associate Senior Engineer at Qiyuan Lab, where he has spearheaded numerous high-impact projects in computer vision, AI, and simulation technologies. Over his career, he has successfully completed 28 research and consultancy projects, including a notable collaboration with Baidu to develop an AI-powered medical Q&A system that significantly improved user engagement metrics. His career highlights include leading teams in the development of large-scale model algorithms, simulation scene construction, and few-shot object recognition frameworks. Shantao’s practical experience is reinforced by 14 published or in-process patents and multiple software development achievements, including tools for multi-type algorithm execution and sonar simulation imaging. His work has consistently demonstrated high relevance to industry needs and national innovation strategies. Recognized with the Baidu Best Engineer Award, Shantao continues to push the boundaries of applied AI and intelligent systems. He is also actively involved in the Chinese Institute of Command and Control, enhancing his contributions to the field.

Research Focus

Shantao Ping’s research is primarily centered on computer vision, image processing, natural language processing (NLP), and foundation models. His work addresses critical challenges in simulation scene reconstruction, few-shot object recognition, structured light calibration, and human-computer interaction assisted by large models. He focuses on developing algorithms that integrate simulation with AI to achieve realistic scene modeling and real-time data processing. Shantao is particularly interested in the intersection of AI and simulation, leveraging intelligent algorithms to enhance perception, decision-making, and scene understanding in complex environments. His innovative research in multi-object tracking and global graph matching is paving the way for advanced applications in autonomous systems and smart interaction platforms. Through national patents and practical deployments, he has made significant strides in developing intelligent, scalable solutions that are not only theoretically sound but also practically impactful, contributing directly to the fields of healthcare, simulation technology, and large-scale data interaction.

Publication Top Notes

Multi-view Multi-object Tracking Based on Global Graph Matching Structure (Conference Paper)
- Authors: Shantao Ping, Chao Li, Hao Sheng, Jiahui Chen, Zhang Xiong
- Summary: This work proposes a novel global graph matching framework for tracking multiple objects across multiple viewpoints, significantly improving tracking accuracy in complex scenes.
A Method and Apparatus for Specific Target Reconnaissance by Unmanned Aerial Vehicle (Patent)
- Authors: Shantao Ping, Ying He
- Summary: Introduces a UAV-based reconnaissance system with enhanced precision for specific target detection in dynamic environments.
A Method, Apparatus, and Device for 3D Scene Construction (Patent)
- Authors: Shantao Ping, Xulong Ma, Ying He
- Summary: Details a system for efficient 3D scene modeling using intelligent algorithms, optimizing both speed and accuracy.
Method for Human-Computer Interaction Assisted by Large Models (Patent)
- Authors: Shantao Ping, Xulong Ma, Ying He, Xiaoqiang Jin, Pinjie Li, Qianchuan Zhao
- Summary: Presents a human-computer interaction framework enhanced by large foundational models for improved user experience and system adaptability.
Method, Apparatus, Device, and Storage Medium for Generating Sonar Simulated Images (Patent)
- Authors: Shantao Ping, Xulong Ma, Ying He, Jiacheng Li
- Summary: Describes a sonar image simulation method that increases the fidelity and reliability of underwater detection simulations.

Conclusion

Shantao Ping is a highly capable, application-driven researcher with an impressive track record of industry-relevant projects, innovative patents, and impactful collaborations, particularly in AI and computer vision. The strong applied research portfolio and demonstrated ability to solve real-world problems make him a solid candidate for the Best Researcher Award. However, to fully align with the traditional benchmarks of this award (which often emphasize academic citations and international recognition), increasing the number of SCI/Scopus journal publications, improving citation metrics, and pursuing more visible academic leadership roles would be beneficial.

Prof Chuen-Horng Lin | Computer Vision | Best Researcher Award

Prof Chuen-Horng Lin, National Taichung University of Science and Technology, Taiwan

Chuen-Horng Lin, Ph.D., is a distinguished Professor in the Department of Computer Science and Information Engineering at National Taichung University of Science and Technology, Taiwan. He earned his M.S. and Ph.D. in Applied Mathematics from National Chung-Hsing University, Taiwan. Dr. Lin’s research focuses on computer vision, image processing, machine learning, deep learning, and pattern recognition. His work specifically delves into automated analysis of images and videos through advanced detection, tracking, and segmentation methods. With numerous publications and a robust academic presence, Dr. Lin continues to contribute significantly to the field, bridging theoretical advancements with practical applications in digital imaging technologies.

Publication Profile

Google Shcolar

Education

Chuen-Horng Lin earned both his Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in Applied Mathematics from National Chung-Hsing University, Taiwan. His academic journey at one of Taiwan’s leading institutions equipped him with a strong foundation in mathematical principles and their applications. This rigorous training provided him with essential skills and insights that have been pivotal to his subsequent career in computer science and engineering. Dr. Lin’s educational background continues to underpin his research and contributions to fields such as computer vision, image processing, machine learning, deep learning, and pattern recognition, shaping his expertise in automated image and video analysis techniques.

Research focus

Chuen-Horng Lin’s research spans several interdisciplinary areas, with a primary focus on advancing techniques in computer vision, image processing, and machine learning. His work is characterized by pioneering contributions to content-based image retrieval systems, leveraging color, texture, and spatial features for enhanced accuracy and efficiency. Dr. Lin has also delved into the development of algorithms like the fast K-means method tailored for image retrieval, and the application of adaptive features using genetic algorithms for image classification. Additionally, he has made significant contributions to queueing theory and optimization models, particularly in multi-server systems and queueing analysis with fuzzy parameters, showcasing his broad expertise in both theoretical and applied aspects of computational sciences.