Zhiguo Shi | Big Data Analytics | Outstanding Scientist Award

Published on by Business Global Awards

Outstanding Scientist Award

Zhiguo Shi
Big Data Research Center, University of Science and Technology Beijing, China
Zhiguo Shi
Affiliation Big Data Research Center, University of Science and Technology Beijing
Country China
Scopus ID 57199774338
Documents 79
Citations 386
h-index 9
Subject Area Big Data Analytics
Event Business Global Awards

Zhiguo Shi, a professor and doctoral supervisor associated with the Big Data Research Center at the University of Science and Technology Beijing. His academic and professional activities are primarily concentrated in the fields of big data analytics, intelligent systems, digital governance, urban data infrastructure, and artificial intelligence applications. Over the course of his academic career, Shi has contributed to research initiatives, technological innovation programs, government-supported digital infrastructure projects, and standards development activities related to large-scale data systems and intelligent computing environments.[1]

Abstract

Zhiguo Shi has established an academic profile centered on the integration of big data systems, intelligent analytics, urban digital infrastructure, and artificial intelligence applications. His research activities have included participation in national and regional research initiatives, development of government-oriented data governance systems, and publication of scholarly work related to data-driven intelligent systems. Shi has additionally contributed to the formulation of technical standards and platform-level innovation projects connected to public-sector digital transformation and smart city technologies.[2]

Keywords

Big Data Analytics, Artificial Intelligence, Intelligent Systems, Smart Cities, Urban Data Platforms, Digital Governance, Data Infrastructure, Machine Learning, Scientific Research, Data Standardization

Introduction

The increasing importance of data-driven technologies in governance, industry, and scientific research has led to expanded academic attention toward big data analytics and intelligent computational systems. Researchers working in this field often contribute not only through theoretical advances, but also through the implementation of scalable data infrastructures and applied innovation initiatives. Within this context, Zhiguo Shi has been associated with multiple projects involving data governance, AI-enabled applications, and intelligent urban systems in China.[1]

Zhiguo Shi obtained his doctoral degree in engineering from the Institute of Software of the Chinese Academy of Sciences in 2008. Following his doctoral studies, he joined the University of Science and Technology Beijing and progressed through academic positions including lecturer, associate professor, and professor. His leadership roles have included service as Director of the Big Data Research Center and participation in multiple scientific committees and laboratory initiatives focused on data systems and future city technologies.[3]

Research Profile

Zhiguo Shi’s research profile combines academic scholarship with applied technological development in public-sector and industrial environments. His documented research interests include big data systems, intelligent computing architectures, data governance methodologies, machine learning applications, and smart urban management technologies.[2]

He has supervised and participated in multiple nationally funded scientific projects, including initiatives supported by the National Natural Science Foundation of China and collaborative technology programs related to public digital infrastructure. His research activities have addressed psychological modeling through expression sequence analysis, blockchain-enabled governance platforms, AI industry service systems, and urban knowledge-base development projects for emerging smart city ecosystems.[4]

  • Big data analytics and intelligent systems research
  • Government-oriented data governance and management systems
  • Artificial intelligence infrastructure and application platforms
  • Urban digitalization and smart city technologies
  • Technical standards and data platform engineering

Research Contributions

Zhiguo Shi has contributed to the advancement of data infrastructure systems and public-sector digital governance through a combination of academic research and applied technological development. He has participated in the drafting and implementation of technical standards associated with government data platforms and digital governance frameworks.[2]

His professional activities also include leadership roles in major operational projects involving Beijing’s large-scale data management systems. These initiatives included platform operation services, data aggregation capabilities, AI infrastructure deployment, and smart sensing systems intended to support administrative efficiency and digital governance modernization.[4]

In addition to platform engineering activities, Shi has participated in interdisciplinary innovation efforts associated with future city technologies, intelligent robotics competitions, and vertical large-model applications. These activities demonstrate a continuing focus on translating academic knowledge into scalable technological applications.

Publications

Zhiguo Shi has authored and co-authored numerous scholarly publications related to big data systems, intelligent analytics, and applied artificial intelligence. His publication portfolio includes journal articles, conference proceedings, technical reports, and standards-oriented contributions.[1]

  • More than 79 indexed academic documents
  • Research publications in big data and intelligent systems
  • Participation in government and industry technical standards
  • Multiple invention patents associated with digital systems research
  • Collaborative research contributions in smart governance technologies

Selected scholarly outputs and indexing records are associated with DOI-based academic referencing systems and Scopus indexing databases.

Research Impact

Zhiguo Shi can be observed through his combination of academic publishing activity, project leadership, technology deployment, and educational contributions. His work has supported the development of operational data systems and governance technologies in Beijing and other emerging urban innovation environments.[2]

Zhiguo Shi has also received recognition in teaching and innovation-related activities. These include distinctions connected to higher education teaching performance, scientific advancement, and applied technology competitions. His contributions illustrate the interdisciplinary relationship between academic scholarship and public digital transformation.[3]

Award Suitability

Zhiguo Shi demonstrates characteristics commonly associated with recognition through scientific achievement awards. His documented contributions include interdisciplinary research, scientific publication activity, leadership in major data infrastructure projects, participation in national and regional innovation programs, and contributions to technical standardization.[2]

His involvement in large-scale public digital systems and intelligent platform development further reflects the practical relevance of his research activities. These accomplishments align with evaluation criteria frequently associated with international scientific recognition programs focused on innovation, impact, research productivity, and technological advancement.[4]

Conclusion

Zhiguo Shi has developed a multidisciplinary academic and technological profile centered on big data analytics, intelligent systems, digital governance, and smart urban technologies. Through research publications, project leadership, standards development, and applied innovation activities, he has contributed to the evolving landscape of data-driven infrastructure and intelligent public systems. His academic record and professional engagement support the relevance of his recognition within the context of the Outstanding Scientist Award presented through the Business Global Awards framework.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Zhiguo Shi, Author ID 57199774338. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57199774338
  2. H Chang, S Han, Z Shi, S Hu 2023 9th International Conference on Systems and Informatics (ICSAI), 2023. Limited resources multi-object tracking by utilizing motion cue from the meanshift algorithm
    https://ieeexplore.ieee.org/abstract/document/10423322/
  3. S Hu, X Zhang, Z Shi 2023 2nd International Conference on Machine Learning, Control …, 2023.> Student Expression Recognition Algorithm Based on TransConv
    https://ieeexplore.ieee.org/abstract/document/10475474/
  4. X Yao,. & Z Shi, XJ Du. Concurrency and Computation: Practice and Experience, (2023) Android malware detection based on sensitive features combination
    https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.7555

Shiyi Ren | Machine Learning | Best Researcher Award

Published on by Business Global Awards

Mr. Shiyi Ren – Machine Learning  – Best Researcher Award

The University of Auckland  | New Zealand

Author Profile 

Early Academic Pursuits 🎓

Chengdu University (09/2014 – 06/2018)

Bachelor of Engineering in Vehicle Engineering

He began his academic journey at Chengdu University, where he pursued a Bachelor of Engineering in Vehicle Engineering. This foundational period was instrumental in shaping his technical skills and understanding of engineering principles. His undergraduate studies laid the groundwork for his future research endeavors, particularly in the fields of machine vision and robotics.

Sichuan Agricultural University (09/2018 – 06/2021)

Master of Agriculture in Agricultural Engineering and Information Technology

He continued his academic pursuits at Sichuan Agricultural University, earning a Master of Agriculture in Agricultural Engineering and Information Technology. His master’s research focused on the integration and demonstration of key technologies in the mechanization of potato production, specifically in hilly areas. This project (2018YFD0701103) involved the development and testing of a potato harvester, including the research of potato excavating parts, potato soil separation screens, and soil crushing rollers. This experience honed his skills in agricultural machinery and set the stage for his future research in mechatronics and automation.

 Professional Endeavors 💼

The University of Auckland (05/2022 – Present)

PhD Candidate of Mechatronics Engineering

Currently, he is a PhD candidate at The University of Auckland in the field of Mechatronics Engineering. His research focuses on advanced topics such as machine vision and deep learning. He is involved in pioneering projects, including the real-time prediction and measurement of the in vitro robotic food chewing process. His work aims to enhance the efficiency and accuracy of robotic systems through innovative technology integration.

Internship at CRRC Ziyang Co., Ltd (24/02/2020 – 26/03/2020)

Position: Crankshaft Engineer

During his internship at CRRC Ziyang Co., Ltd, Ren worked as a Crankshaft Engineer. He was responsible for the design of crankshafts, an experience that further solidified his practical engineering skills and his ability to apply theoretical knowledge in real-world industrial settings.

Contributions and Research Focus on Machine Learning 📚

His contributions to the field of engineering are substantial and varied. His research interests include machine vision, deep learning, and agricultural automation. His notable projects include:

  • Machine Vision and Deep Learning-Based Real-time Prediction and Measurement of the In Vitro Robotic Food Chewing Process (01/05/2018 – Present): This ongoing project aims to enhance the predictive capabilities of robotic systems using advanced machine vision and deep learning techniques.
  • Integration and Demonstration of Key Technologies in the Whole Mechanization of Potato Production on a Moderate Scale in Hilly Areas (2018YFD0701103) (01/09/2018 – 01/09/2022): This project focused on developing efficient and effective mechanized solutions for potato harvesting in challenging terrains.

Published Papers and Patents

  • Ren, S., Chen, B., Dhupia, J. S., Stommel, M., & Xu, W. (2023, October). “A Deep Learning System to Quantify and Predict the Chewing Process of Foods.” In ASME International Mechanical Engineering Congress and Exposition (Vol. 87639, p. V006T07A092). American Society of Mechanical Engineers.
  • Ren, S., Chen, B., Wang, X., Dhupia, J., Stommel, M., & Xu, W. (2023, November). “Concept of Real-Time Prediction and Evaluation System of Robotic Food Chewing Using Machine Vision and Deep Learning.” In 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (pp. 1-6). IEEE.
  • Zürn, M., Kienzlen, A., Klingel, L., Lechler, A., Verl, A., Ren, S., & Xu, W. (2023, August). “Deep Learning-Based Instance Segmentation for Feature Extraction of Branched Deformable Linear Objects for Robotic Manipulation.” In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) (pp. 1-6). IEEE.
  • Ren, S., Wu, X., Shi, D., Jikui, A., & Lv, X. “A Potato Earth Breaking Excavating Device” (Authorization Notice No.: CN 209489155 U).
  • Fu, Y., Ren, S. Y., Tang, P., Leng, Y. C., Chen, X. H., Tu, X. Y., & Lv, X. R. (2023). “Design and simulation test of digging device for small potato harvester.”
  • Wu, T., Zhang, Z., Ren, S., Guo, L., Zhang, Y., & Zeng, Y. (2023, November). “A Hybrid Evolutionary Algorithm for Stochastic Robot Disassembly Line Balancing Problem.” In 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (pp. 1-6). IEEE.
  • Tang, P., Ren, S., Jikui, A., Liu, Z., & Lv, X. (2021). “Design of a potato stubble cutting machine for side delivery.” In E3S Web of Conferences (Vol. 260, p. 03018). EDP Sciences.
  • Jikui, A., Lv, X., Ren, S., & Yang, H. (2019, April). “Design of hydraulic corn picking device.” In 2019 3rd International Forum on Environment, Materials and Energy (IFEME 2019) (pp. 130-133). Atlantis Press.

 Accolades and Recognition 🏆

His academic excellence has been recognized through several scholarships and awards:

  • 2nd-class scholarship (11/2018, 11/2019)
  • 3rd-class scholarship (11/2020)
  • 2nd Prize of Campus Integrity Culture Competition (10/2020)
  • China Scholarship Council (05/2022)

 Impact and Influence 🌍

His research has a significant impact on both the academic community and practical applications. His work in machine vision and deep learning for robotic systems contributes to advancements in automation technology, with potential applications in various industries, including agriculture and manufacturing. His contributions are particularly relevant in enhancing the efficiency and sustainability of agricultural practices.

 Legacy and Future Contributions 🔮

He is poised to leave a lasting legacy in the fields of mechatronics and agricultural engineering. His innovative research and practical contributions to mechanized systems and robotic technology pave the way for future advancements. As he continues his PhD at The University of Auckland, his ongoing projects and future endeavors are expected to further enhance the field and inspire future researchers and engineers.

Citations

Citations          3

h-index            1

i10-index         0

Notable Publications