Prof. Hong Zheng | Computational Mechanics | Best Researcher Award

Posted on by Academic achievements

Prof. Hong Zheng | Computational Mechanics | Best Researcher Award

Prof. Hong Zheng | Computational Mechanics – Beijing University of Technology, China

Prof. Hong Zheng is a highly accomplished academic and researcher in the field of geotechnical and computational civil engineering. With more than three decades of research experience, he has become a key figure in the development of numerical modeling methods for rock and soil mechanics. His scholarly work integrates traditional engineering models with modern computational approaches, particularly artificial intelligence and numerical manifold methods, making his research widely applicable and forward-looking in civil infrastructure and geomechanical analysis.

Profile Verified:

Orcid | Scopus 

Education:

Prof. Zheng earned his Ph.D. in Civil Engineering from Beijing University of Technology. His doctoral training focused on structural and geotechnical modeling, providing him with a strong foundation in both theoretical and applied mechanics. His academic excellence during this period shaped the trajectory of his research in advanced numerical techniques for solving complex civil engineering problems.

Experience:

Prof. Zheng’s professional experience spans several renowned institutions. He began his research career at the Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, where he worked for over two decades (1988–2013), contributing extensively to slope stability and rock mechanics research. From 2001 to 2014, he was affiliated with China Three Gorges University, participating in research projects related to dam safety and hydropower infrastructure. Since 2013, he has been a full-time faculty member at Beijing University of Technology, where he is actively involved in teaching, supervising Ph.D. students, and leading research initiatives in computational geomechanics.

Research Interests:

Prof. Zheng’s research interests center around advanced computational methods for civil and geotechnical engineering problems. He specializes in the Numerical Manifold Method (NMM), Finite-Discrete Element Method (FDEM), and deep learning applications for slope and tunnel stability analysis. His recent work includes physics-informed neural networks for 3D seepage prediction and hybrid numerical-AI models for complex unconfined flow problems. His interdisciplinary approach addresses real-world engineering challenges with innovative computational techniques.

Awards:

While not formally listed with individual honors, Prof. Zheng’s recognition comes through consistent publications in prestigious international journals, extensive citation by peers, and influential roles in large-scale engineering projects. His sustained academic output, institutional leadership, and role as a mentor to numerous graduate students underscore his eligibility for high-level research recognition.

Selected Publications:

  • 🧠 “The pre-trained explainable deep learning model with stacked denoising autoencoders for slope stability analysis” (2024, Engineering Analysis with Boundary Elements) – cited by 12 articles.
  • 🌊 “Three-dimensional seepage analysis for the tunnel in nonhomogeneous porous media with physics-informed deep learning” (2025, Engineering Analysis with Boundary Elements) – cited by 8 articles.
  • 🧱 “Modeling variably saturated flows in porous media using the numerical manifold method” (2024, Engineering Analysis with Boundary Elements) – cited by 10 articles.
  • 🧩 “Boundary settings for seismic dynamic analysis of rock masses using the nodal-based continuous-discontinuous deformation analysis method” (2025, Computer Methods in Applied Mechanics and Engineering) – cited by 7 articles.
  • ⚙️ “Preconditioned smoothed numerical manifold methods with unfitted meshes” (2023, International Journal for Numerical Methods in Engineering) – cited by 15 articles.
  • 🔍 “A new procedure for locating free surfaces of complex unconfined seepage problems using fixed meshes” (2024, Computers and Geotechnics) – cited by 6 articles.
  • 🧮 “Shear band static evolution based on complementarity method and the improved numerical manifold method” (2024, Engineering Analysis with Boundary Elements) – cited by 9 articles.

Conclusion:

In summary, Prof. Hong Zheng exemplifies the profile of a highly innovative, dedicated, and impactful researcher. His extensive career in academia, combined with deep technical knowledge and modern interdisciplinary integration, positions him as an ideal candidate for the Best Researcher Award. His research has not only advanced the academic understanding of geomechanical processes but also contributed to the safety and sustainability of large civil infrastructure. His commitment to excellence, mentorship, and research leadership continues to shape the field and inspire emerging engineers worldwide.

 

 

Yingyuan Liu | Engineering | Women Researcher Award

Posted on by Academic achievements

Ms. Yingyuan Liu | Engineering | Women Researcher Award

Professor | Shanghai Normal university | China

Dr. Liu Yingyuan is an accomplished researcher and faculty member specializing in the application of artificial intelligence (AI) in fluid machinery. With a strong academic foundation and extensive professional experience, she has contributed significantly to advancing machine learning models, turbulence analysis, airfoil optimization, and fault diagnosis. Currently serving at Shanghai Normal University, Dr. Liu’s expertise bridges the intersection of AI and fluid mechanics, making her a leader in her field.

Profile

Scopus

Education

Dr. Liu Yingyuan earned her Ph.D. in Fluid Machinery from Zhejiang University in 2016, where she focused on the intricate dynamics of fluid mechanics and advanced computational methods. Her undergraduate studies in Process Equipment and Control Engineering at the China University of Petroleum (East China), completed in 2011, laid a strong foundation in engineering principles and process optimization.

Experience

Dr. Liu has been a faculty member at Shanghai Normal University, where she combines her deep research expertise with her passion for teaching. Her academic career is marked by impactful research, collaborative projects, and mentorship of students, particularly in the realm of AI applications in fluid mechanics. Her contributions extend beyond academia through her active engagement in professional committees and collaborations with industry experts.

Research Interests

Dr. Liu’s research is centered on leveraging artificial intelligence technologies to address complex challenges in fluid machinery. Her interests include machine learning modeling for turbulence, optimal airfoil shape design, and fault diagnosis in fluid machinery. By integrating AI with engineering, she has developed innovative solutions that enhance the efficiency and reliability of mechanical systems.

Awards

Dr. Liu’s innovative research has garnered recognition in the academic and professional community. Notably, her studies in machine learning-driven fault diagnosis and airfoil optimization have earned her nominations for awards in engineering and AI applications. Her commitment to excellence continues to inspire peers and students alike.

Publications

  1. Liu YY, Shen JX, Yang PP, Yang XW. A CNN-PINN-DRL driven method for shape optimization of airfoils. Engineering Application of Computational Fluid Mechanics, 2025, 19(1): 2445144.
    • Cited by: Researchers developing AI-driven aerodynamics models.
  2. Shen JX, Liu YY, Wang Leqin.* A Deep Learning-Based Method for Airfoil Parametric Modeling. Chinese Journal of Engineering Design, 2024, 31(03): 292-300.
    • Cited by: Articles on parametric modeling techniques.
  3. Liu D, Liu YY. A Deep Learning-Based Fault Diagnosis Method for Fluid Machinery with Small Samples. Journal of Shanghai Normal University (Natural Sciences), 2023, 52(02): 264-271.
    • Cited by: Studies on fault diagnosis in mechanical systems.
  4. Liu YY, Gong JG, An K, Wang LQ. Cavitation Characteristics and Hydrodynamic Radial Forces of a Reversible Pump–Turbine at Pump Mode. Journal of Energy Engineering, 2020, 146(6): 04020066.
    • Cited by: Publications on hydrodynamics and pump-turbine systems.
  5. Liu Y Y, An K, Liu H, et al. Numerical and experimental studies on flow performances and hydraulic radial forces of an internal gear pump with a high pressure. Engineering Applications of Computational Fluid Mechanics, 2019, 13: 1, 1130-1143.
    • Cited by: Research focused on internal gear pump performance.
  6. Liu Y Y, Wang L Q, Zhu Z C.* Experimental and numerical studies on the effect of inlet pressure on cavitating flows in rotor pumps. Journal of Engineering Research, 2016, 4(2): 151-171.
    • Cited by: Studies on cavitation phenomena in rotor pumps.
  7. Liu Y Y, Wang L Q, Zhu Z C.* Numerical study on flow characteristics of rotor pumps including cavitation. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2015, 229(14): 2626-2638.
    • Cited by: Articles on numerical modeling of fluid flows.

Conclusion

Dr. Liu Yingyuan exemplifies the integration of advanced engineering knowledge and AI-driven innovation. Her pioneering contributions to the fields of fluid mechanics and machinery have not only pushed technological boundaries but also inspired the next generation of engineers and researchers. Dr. Liu’s work continues to serve as a cornerstone for advancements in intelligent mechanical systems, ensuring her lasting impact on both academia and industry.

Aziz Hassan Shekh-Abed | Electrical Engineering and Computer Engineering | Best Researcher Award

Posted on by Academic achievements

Dr. Aziz Hassan Shekh-Abed | Electrical Engineering and Computer Engineering | Best Researcher Award

Lecturer | Ruppin Academic Center | Israel

Short Bio 📚

Aziz Shekh-Abed is a dedicated academic professional with a robust background in technology education and engineering. Currently, a lecturer at Ruppin Academic Center’s Department of Electrical and Computer Engineering, Aziz has a track record of inspiring students through engaging lectures and innovative teaching methods. His career is marked by a strong commitment to enhancing student comprehension and fostering an environment conducive to learning.

Profile 

ORCID

Education 🎓

Aziz Shekh-Abed’s educational journey is both extensive and impressive. He earned his BScTE in Technology Education from Tel Aviv University in 2001, followed by an MA in Education from the same institution in 2006. In 2020, he completed his PhD at the Technion – Israel Institute of Technology, specializing in Education in Science and Technology. His doctoral research focused on systems thinking and abstract thinking among high-school students.

Experience 💼

Aziz’s teaching career spans over two decades. He has taught at various institutions, including Amal Science and Technology College and Nuns’ School in Nazareth. Since 2021, he has been a lecturer at Ruppin Academic Center, where he has also served as the Coordinator of the Retention Committee. His experience includes developing course materials, mentoring students, and participating in academic conferences worldwide.

Research Interest 🔬

Aziz’s research interests lie in the intersection of technology education, systems thinking, and abstract thinking. He is particularly interested in how these cognitive skills can be enhanced through project-based learning and the integration of dedicated tasks. His work often explores the impact of innovative teaching methods on student performance and learning outcomes.

Awards 🏆

Throughout his academic journey, Aziz has received several accolades. Notably, he was awarded a PhD scholarship from 2016 to 2020. Additionally, he has received letters of appreciation for mentoring high school students who won various project competitions, highlighting his impact on the educational community.

Publications 📄

Aziz Shekh-Abed has an impressive portfolio of publications. Here are some notable works:

  1. Interrelations between systems thinking and abstract thinking: the case of high-school electronics students – European Journal of Engineering Education, 2021.
  2. Promoting systems thinking and abstract thinking in high-school electronics students: Integration of Dedicated Tasks into Project-Based Learning – International Journal of Engineering Education, 2021.
  3. Challenges and opportunities for higher engineering education during the COVID-19 Pandemic – International Journal of Engineering Education, 2022.
  4. Challenges to Systems Thinking and Abstract Thinking Education During the COVID-19 Pandemic – International Journal of Engineering Education, 2023.
  5. Relationships between Reflection Ability and Learning Performance of junior electronics engineering students – International Journal of Engineering Education, 2023.