Afera Halefom Teka | Engineering | Research Excellance Award

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Mr. Afera Halefom Teka | Engineering | Research Excellance Award

Afera Halefom Teka | University of Chinese Academy of Sciences | Ethiopia

Mr. Afera Halefom Teka is a researcher specializing in cartography, geospatial analysis, hydrology, and land–environment interactions, with strong expertise in GIS, remote sensing, and water resources modeling. His work addresses land use change, hydrological processes, watershed vulnerability, and environmental sustainability across diverse landscapes. With experience in academic teaching, research leadership, and interdisciplinary collaborations, he contributes to evidence-based geospatial solutions for climate resilience, watershed management, and sustainable land–water governance. His research applies spatial modeling, multi-criteria evaluation, machine learning, and advanced cartographic visualization to examine land use dynamics, climate variability, soil erosion risk, groundwater potential, and environmental change detection. He has also taken part in international trainings, conferences, and collaborative projects advancing geospatial applications for disaster risk reduction and resource planning. His contributions have been recognized through academic distinctions, research committee leadership roles, competitive training selections, and conference acknowledgments.

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Featured Publications

Wei Jiang | Engineering | Editorial Board Member

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Assoc. Prof. Dr. Wei Jiang | Engineering | Editorial Board Member

Associate Dean | Changzhou Institute of Technology | China

Assoc. Prof. Dr. Wei Jiang is an Associate Professor and academic leader specializing in aerospace engineering, aircraft dynamics, structural safety, turbulence response, and reliability-based design. His work integrates advanced modeling with applied engineering to enhance flight safety, structural health monitoring, and high-precision measurement technologies. With significant experience in multidisciplinary research and leadership roles, he has contributed to major scientific projects, industry–academia collaborations, and the development of innovative methods for analyzing nonlinear aircraft behavior under complex atmospheric conditions. His research also extends to precision measurement, tribology, and applied computational analysis, supporting advancements in aircraft performance, predictive maintenance, and structural optimization. His contributions have been recognized through multiple provincial-level honors that acknowledge his impact on engineering innovation and scientific development.

Profile : Scopus 

Featured Publictions 

Chen, J., Chen, Z., & Jiang, W. (2025). A reliability-based design optimization strategy using quantile surrogates by improved PC-kriging. Reliability Engineering & System Safety. Cited by: N/A.

Jiang, W., Guo, H., Li, Z., & Chang, R. C. (2024). Nonlinear unsteady behaviour study for jet transport aircraft response to serious atmospheric turbulence. The Aeronautical Journal. Cited by: N/A.

Jiang, W., Guo, H., Zhu, D., & Chang, R. C. (2024). Optimization of flight conditions based on performance sensitivity analysis for jet transport aircraft. Aircraft Engineering and Aerospace Technology. Cited by: N/A.

Jiang, W., Chang, R. C., Yang, N., & Xu, Y. (2023). Severity assessment of sudden plunging motion for jet transport aircraft in severe turbulence. Aircraft Engineering and Aerospace Technology. Cited by: N/A.

Jiang, W., Chang, R. C., Zhang, S., & Zang, S. (2023). Structural health monitoring and flight safety warning for aging transport aircraft. Journal of Aerospace Engineering. Cited by: N/A.

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.

 

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.