Mr. Chao Wang | Computer Science and Artificial Intelligence | Research Excellence Award

North China University of Technology | China

Dr. Chao Wang, an accomplished Associate Professor at the North China University of Technology, is a distinguished researcher whose work significantly advances the fields of vehicular networks, IoT security, and edge computing. Holding a Ph.D. in Computer Science, Dr. Wang has developed a strong academic portfolio grounded in deep technical expertise and innovative thinking. His research addresses some of the most pressing challenges in intelligent transportation systems, focusing on secure data communication, privacy-preserving mechanisms, and efficient resource allocation in highly dynamic vehicular environments. With 23 publications in SCI and Scopus-indexed journals and conferences, his work demonstrates a consistent trajectory of high-quality scientific output. His research impact is further reflected in 660 citations, an H-index of 10, and an i10-index of 10, according to Google Scholar as of December 3, 2025. These metrics underscore his growing global influence and the relevance of his contributions to next-generation intelligent mobility systems. Dr. Wang has successfully completed and continues to lead multiple national and provincial research projects, focusing on enhancing the reliability, safety, and intelligence of connected vehicle ecosystems. His innovations include blockchain-based frameworks for secure traffic data management, anomaly detection systems for vehicle-to-vehicle communication, and privacy-preserving architectures for IoT-enabled transportation infrastructures. With four patents published or under process, he demonstrates strong translational capability, often transforming theoretical models into practical, real-world solutions. His collaborations with researchers from Springer Nature, IEEE, and various international universities highlight his interdisciplinary approach and commitment to advancing global research partnerships. Although he has not yet undertaken industry consultancy projects, Dr. Wang’s research outputs inherently serve industrial needs, especially in smart transportation, urban planning, and secure IoT deployment. He is also an active professional member of IEEE, contributing to the broader scientific community through peer review, academic exchanges, and participation in scholarly networks. Beyond research, Dr. Wang is dedicated to academic mentorship, guiding students who have achieved recognition in national-level competitions, illustrating his commitment to nurturing the next generation of innovators. With strong expertise, a solid publication record, impactful innovations, and a dedication to advancing secure and intelligent transportation systems, Dr. Wang exemplifies the qualities celebrated by the Research Excellence Award. His achievements reflect not only academic rigor but also societal relevance, making him a highly deserving nominee for this international honor.

Profile: Orcid

Featured Publications

Li, J., Wang, C., Seo, D., Cheng, X., He, Y., Sun, L., Xiao, K., & Huo, Y. (2021). Deep learning-based service scheduling mechanism for GreenRSUs in the IoVs. Wireless Communications and Mobile Computing, 2021, Article 7018486. https://doi.org/10.1155/2021/7018486

Wang, C. (2020). Destination prediction-based scheduling algorithms for message delivery in IoVs. IEEE Access, 8, 1–15. https://doi.org/10.1109/ACCESS.2020.2966494

Wang, C. (2018). A blockchain-based privacy-preserving incentive mechanism in crowdsensing applications. IEEE Access, 6, 1–12. https://doi.org/10.1109/ACCESS.2018.2805837

Wang, C. (2015). A reliable broadcast protocol in vehicular ad hoc networks. International Journal of Distributed Sensor Networks, 11(8), Article 286241. https://doi.org/10.1155/2015/286241

Wang, C. (2015). Ads dissemination in vehicular ad hoc networks. In 2015 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICC.2015.7248890

Wang, C. (2014). Schedule algorithms for file transmission in vehicular ad hoc networks. In Wireless Algorithms, Systems, and Applications (pp. 135–147). Springer. https://doi.org/10.1007/978-3-319-07782-6_12

Wang, C. (2014). S-disjunct code-based MAC protocol for reliable broadcast in vehicular ad hoc networks. In 2014 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) (pp. 1–6). IEEE. https://doi.org/10.1109/IIKI.2014.66

Prof. Xu Liu | Statistics | Best Researcher Award

Prof. Xu Liu, Shanghai University of Finance and Economics, China

Prof. Xu Liu, a tenured Full Professor at the School of Statistics and Management, Shanghai University of Finance and Economics (SUFE), is a distinguished expert in high-dimensional data analysis, statistical genetics, and AI-enabled decision-making. With a Ph.D. in Statistics from Yunnan University and postdoctoral training from Northwestern University and Michigan State University, he brings extensive international research experience. His work focuses on machine learning, transfer learning, and deep generative models, with numerous publications in top-tier journals. Prof. Liu actively contributes to academic leadership as an editor and conference organizer, making him a key figure in the field of modern statistics and data science.

Author Profile

Scopus

🎓 Early Academic Pursuits

Prof. Xu Liu’s academic journey is rooted in a deep passion for mathematics and statistics. His formal education began with a Bachelor of Science in Mathematics from Hengyang Normal University (2000–2004). Demonstrating early excellence and commitment to the field, he pursued a Master of Science in Mathematics at Yunnan University, graduating in 2007. His intellectual drive and mathematical acumen culminated in a Ph.D. in Statistics from the same institution in 2011. These formative years laid a strong theoretical foundation and cultivated his research interests in complex data structures, machine learning, and statistical inference.

During his graduate studies, Prof. Liu delved into challenging problems in mathematical modeling, statistical theory, and early explorations in computational statistics, developing an academic rigor that continues to define his work today.

👨‍🏫 Professional Endeavors

Following the completion of his doctoral studies, Prof. Liu embarked on a postdoctoral research path that would span internationally recognized institutions. From 2011 to 2013, he was a postdoctoral researcher in the Department of Statistics at Northwestern University, followed by another impactful postdoc position in the Department of Statistics and Probability at Michigan State University (2013–2016). These roles enabled him to collaborate with prominent statisticians and immerse himself in cutting-edge research on high-dimensional inference and statistical learning.

In 2016, Prof. Liu joined the School of Statistics and Management at Shanghai University of Finance and Economics (SUFE) as an Assistant Professor. Over the years, his academic progression has been steady and well-earned—rising to Associate Professor (2019), achieving tenure in 2022, and recently being promoted to Full Professor in 2024.

His professional tenure at SUFE reflects both his teaching excellence and research productivity, positioning him as a pillar of the department and a leader in statistical education.

📊 Contributions and Research Focus

Prof. Xu Liu’s research contributions span a diverse range of statistical and data science disciplines:

High-dimensional data analysis
Machine learning and deep generative models
Representation and transfer learning
Statistical genetics and gene-environment interactions (G×E and G×G)
Advanced variable selection methods
Uncertainty quantification and statistical inference

His recent publications appear in prestigious journals like Statistics in Medicine, Journal of Multivariate Analysis, Bioinformatics, Journal of Computational and Graphical Statistics, and Statistics in Biosciences. These works include the development of tools such as the Python-based REGS sampler and the R-package qfabs, demonstrating a strong commitment to open-source statistical computing and reproducible science.

He is also contributing significantly to academic literature with three upcoming books focused on AI decision-making and high-dimensional statistical inference, all scheduled for release in 2025.

🏅 Accolades and Recognition

Prof. Liu’s excellence in research and academic contribution has earned him significant accolades:

Outstanding Achievement Award of Philosophy and Social Science, Shanghai (2023)
Third Prize, 17th “Challenge Cup” Shanghai Science and Technology Competition (2022)

These awards underscore his relevance not only in theoretical statistics but also in its impactful applications across economics, social science, and public policy.

Moreover, his professional recognition extends to editorial roles. He serves as:

Associate Editor, Journal of Statistical Theory and Applications
Associate Editor, International Journal of Organizational and Collective Intelligence
Guest Editor, Special Issue in Axioms on Mathematical and Statistical Finance

🌐 Impact and Influence

Prof. Liu’s influence transcends national boundaries through his international collaborations, open-source software contributions, and thought leadership in machine learning applications in health, finance, and genomics. His work on gene-environment interactions is particularly impactful in the area of statistical genetics, with real-world implications for personalized medicine and epidemiological modeling.

As an educator, Prof. Liu has taught a wide spectrum of courses—from foundational subjects like Mathematical Statistics to advanced topics such as Empirical Process Theory and Computer Programming in C/C++. His mentoring of students and junior researchers helps foster the next generation of statisticians and data scientists.

🌟 Legacy and Future Contributions

With a growing legacy built on innovation, scholarship, and mentorship, Prof. Xu Liu stands at the forefront of modern statistical science in China. His upcoming books will serve as valuable references in both academic and applied settings. As a conference organizer and invited speaker, he continues to shape conversations around statistical learning, AI in economics, and computational statistics.

Looking ahead, his work promises deeper integration of AI-enabled modeling, data-driven decision-making, and ethical data science, especially in public health, policy, and business analytics.

✍️Publication Top Notes

📘Subgroup testing in the change-plane Cox model.

Author: Zhang, X., Ren, P., Shi, X. Ma, S. and Liu, X

Journal: Statistics in Medicine

Year: 2025

📘Random projection-based response best-subset selector for ultra-high dimensional multivariate data

Author: Hu, J., Li, T., Liu, X. and Liu, X

Journal: Multivariate Analysis

Year: 2025

📘Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.

Author: Tan, X., Zhang, X., Cui, Y. and Liu, X.

Journal: Bioinformatics

Year: 2024

Xu Liu | Statistics | Best Researcher Award