Zhi Zong | Engineering | Best Researcher Award

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Prof. Dr. Zhi Zong | Engineering | Best Researcher Award

Fuyao University of Science and Technology | China

Professor Zhi Zong is an internationally acclaimed researcher in naval architecture, ocean engineering, computational mechanics, and fluid–structure interaction, widely recognized for his influential contributions to marine hydrodynamics and advanced numerical simulation. With 334 publications, 5,653 citations, and an h-index of 38 (Scopus), his research covers underwater explosion (UNDEX) physics, nonlinear water waves, bubble dynamics, vortex-induced vibration (VIV), unsteady cavitation, water-entry dynamics, and high-fidelity computational fluid mechanics, employing cutting-edge techniques such as SPH, DEM, and data-driven modeling. He has authored over 460 scientific papers, including more than 230 SCI-indexed articles, and has been continuously listed among the Top 2% Scientists globally (2021–2025). His seven authoritative monographs published with Elsevier, Taylor & Francis/CRC, and Science Press span differential quadrature methods, solitary wave theory, computational underwater explosion mechanics, and bubble damage modeling. Professor Zong’s research has significantly advanced understanding of shock loading on marine structures, hydrodynamic impact, cavitating and multiphase flows, ice–structure interactions, ship motion reduction, and complex multi-physics simulations, with many of his highly cited publications regarded as landmark contributions to SPH modeling, multiphase flow analysis, UNDEX damage prediction, and VIV dynamics.

Profiles: Scopus| Google Scholar | ResearchGate

Featured Publications 

• Liu, M. B., Liu, G. R., Lam, K. Y., & Zong, Z. (2003). Smoothed particle hydrodynamics for numerical simulation of underwater explosion. Computational Mechanics, 30(2), 106–118.

• Liu, M. B., Liu, G. R., Zong, Z., & Lam, K. Y. (2003). Computer simulation of high explosive explosion using smoothed particle hydrodynamics methodology. Computers & Fluids, 32(3), 305–322.

• Zong, Z., & Zhang, Y. (2009). Advanced differential quadrature methods. Chapman and Hall/CRC.

• Chen, Z., Zong, Z., Liu, M. B., Zou, L., Li, H. T., & Shu, C. (2015). An SPH model for multiphase flows with complex interfaces and large density differences. Journal of Computational Physics, 283, 169–188.

• Zhang, Y. Y., Wang, C. M., Duan, W. H., Xiang, Y., & Zong, Z. (2009). Assessment of continuum mechanics models in predicting buckling strains of single-walled carbon nanotubes. Nanotechnology, 20(39), 395707.

Dan Uchimura | Engineering | Best Researcher Award

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Mr. Dan Uchimura | Engineering | Best Researcher Award

Mr. Dan Uchimura|Kajima Corporation | Japan

Dan Uchimura is an emerging professional in nuclear power plant structural design, currently serving as a designer in the Kajima Corporation Nuclear Power Department. With a Master’s Degree in Architecture from Waseda University, he has swiftly transitioned from academia to industry, applying his expertise in structural systems, safety analysis, and computational modeling. During his graduate studies in Tokyo, he focused on enhancing the resilience and sustainability of energy facilities, developing technical skills in MATLAB, Python, and Excel to simulate structural integrity under extreme conditions. Since joining Kajima, Dan has contributed to the planning and design of nuclear power facilities while spearheading research on integrating non-destructive inspection techniques—especially infrared thermography—into plant systems to detect structural anomalies without operational interruptions. Known for his analytical thinking, precision, and interdisciplinary approach, he collaborates with engineers, material scientists, and safety analysts to deliver reliable, innovative design solutions aligned with stringent safety regulations. His research interests center on advancing inspection technologies, modeling structural behavior under thermal and seismic loads, and exploring AI-driven predictive maintenance systems to enhance safety and efficiency in nuclear infrastructure. Though early in his career, Dan has already earned recognition for his innovative contributions, including commendations for his thesis on resilient energy infrastructure and praise from senior engineers for merging theoretical concepts with practical design solutions.

Profile : ORCID

Featured Publication 

Uchimura, D. (2024). Application of infrared thermography for non-destructive structural inspection in nuclear power facilities. Journal of Structural Engineering and Technology.

Uchimura, D. (2023). Resilient architectural design framework for nuclear power plants. International Journal of Sustainable Energy Infrastructure.

Uchimura, D. (2023). Computational modeling of seismic loads in nuclear plant structures. Journal of Advanced Structural Engineering.

 

Shangshang Wu | Engineering | Best Researcher Award

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Dr. Shangshang Wu | Engineering | Best Researcher Award

Tianjin university | China

Wu Shangshang is a mechanical engineer pursuing her Ph.D. at the School of Mechanical Engineering, Tianjin University in China, where she also completed her B.S. and M.S. in Mechanical Engineering. Her research focuses on underwater gliders, emphasizing hydrodynamic identification, motion behavior analysis, and front-end data processing for acoustic communication. Since her master’s studies, she has worked as a graduate researcher, contributing to both experimental sea trials and theoretical modeling, and has published journal articles and conference papers in marine robotics, acoustics, and signal processing. Wu’s doctoral work advances model-based and data-driven methods to improve hydrodynamic prediction and control under uncertain underwater conditions, supporting the development of reliable seabed vehicles and underwater communication systems. She collaborates closely with colleagues at Tianjin University, including researchers such as Guangwei Lv and Shaoqiong Yang, and her early contributions are gaining citations. Her interests also include neural network–based hybrid modeling, online estimation, and mitigating the effects of environmental factors like sea currents and noise on underwater navigation and sensor performance. While no specific awards are publicly documented, Wu shows strong potential in combining experimental insights with computational techniques to enhance the design, control, and stability of underwater gliders.

Profile : Scopus| ORCID  

Featured Publications

AuthorLastName, A. A., & AuthorLastName, B. B. Model and data-driven hydrodynamic identification and prediction for underwater gliders. Physics of Fluids.

AuthorLastName, A. A., & AuthorLastName, B. B. An enhanced variational mode decomposition method for processing hydrodynamic data of underwater gliders. Measurement.

AuthorLastName, A. A., & AuthorLastName, B. B. Multi-body modelling and analysis of the motion platform for underwater acoustic dynamic communication. Applied Mathematical Modelling.

Jingyi Gao | Engineering | Best Researcher Award

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Ms. Jingyi Gao | University of Virginia | United States

Ms. Jingyi Gao | University of Virginia | United States

Jingyi Gao is a Ph.D. candidate in Systems and Information Engineering at the University of Virginia with a 3.75 GPA, focusing on time series prediction, Bayesian probabilistic modeling, and federated learning. She holds an M.S. in Applied Mathematics and Statistics from the Johns Hopkins University (GPA 3.9) and dual bachelor’s degrees in Mathematics–Computer Science and Economics from the University of California, San Diego. Jingyi has extensive teaching experience, serving as a teaching assistant at UVA where she has instructed over 1,000 students across multiple courses in statistical modeling, data mining, AI, and big data systems, and previously supported courses at Johns Hopkins and UC San Diego. She has mentored underrepresented students through the Data Justice Academy and completed research internships at the University of Pittsburgh and Tencent, developing machine learning models for stress detection, healthcare data analysis, and cloud resource forecasting. Jingyi has authored several publications, including work accepted by Pattern Recognition and under review at AAAI and IISE Transactions. Her recent projects involve designing deep latent variable models for ergonomic risk assessment, developing real-time adaptive prediction frameworks for occupational health monitoring, creating federated learning approaches for multi-output Gaussian processes, and modeling behavioral regularity and predictability from multidimensional sensing signals. Combining expertise in machine learning, statistical modeling, and data-driven decision systems, Jingyi aims to advance human-centered intelligent systems through interpretable and privacy-preserving predictive modeling.

Profile: Scopus | Google Scholar

Featured Publications 

Gao, J., Rahman, A., Lim, S., & Chung, S. TimeSets: A real-time adaptive prediction framework for multivariate time series (Manuscript under review at the Association for the Advancement of Artificial Intelligence).

Gao, J., Lim, S., & Chung, S. Gait-based hand load estimation via deep latent variable models with auxiliary information (Manuscript under review at IISE Transactions).

Gao, J., & Chung, S. Federated automatic latent variable selection in multi-output Gaussian processes (Accepted for publication in Pattern Recognition)*.

Gao, J., Yan, R., & Doryab, A. Modeling regularity and predictability in human behavior from multidimensional sensing signals and personal characteristics. Proceedings of the International Conference on Machine Learning and Applications (ICMLA). Institute of Electrical and Electronics Engineers.

Chen, T., Chen, Y., Gao, J., Gao, P., Moon, J. H., Ren, J., … & Woolf, T. B. Machine learning to summarize and provide context for sleep and eating schedules. bioRxiv.