Assoc. Prof. Dr. Hui Zhang | Artificial Intelligence | Industry Achievement Award

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Assoc. Prof. Dr. Hui Zhang | Artificial Intelligence | Industry Achievement Award

Assoc. Prof. Dr. Hui Zhang, Guizhou University of Finance and Economics, China

Hui Zhang is a distinguished senior engineer and associate professor at the School of Information, Guizhou University of Finance and Economics, China. With a Ph.D. in Computational Mathematics from Guizhou Normal University, Zhang has a diverse background spanning both academia and industry. His expertise ranges from computational mathematics to big data and cloud computing, having held prominent roles in R&D departments in China’s tech industry. Additionally, Zhang has served as a reviewer for prestigious journals, contributed to key projects, and holds multiple patents in data science and technology. His ongoing research and professional services make him a well-recognized expert in his field.

Professional Profile

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Summary of Suitability for the Research for Industry Achievement Award

Dr. Hui Zhang demonstrates a unique combination of academic and industrial achievements, making him a strong candidate for the Research for Industry Achievement Award. His background in computational mathematics and computer science, coupled with his experience in R&D and leadership roles in the big data industry, aligns well with the award’s focus on impactful industrial contributions.

πŸŽ“    Education

Hui Zhang completed his Bachelor’s in Information and Computational Science from Guizhou Normal University in 2010, alongside a Bachelor’s in English Education. He pursued a Master’s in Computational Mathematics at the same institution from 2010 to 2013. Hui continued his academic journey by earning a Ph.D. in Computational Mathematics, focusing on innovative computational methods. His educational experience highlights a strong foundation in both mathematics and interdisciplinary learning, setting the stage for his research and teaching career at Guizhou University of Finance and Economics.

πŸ’Ό   Experience

Hui Zhang’s career includes a range of positions in both academia and industry. After completing his master’s degree, he worked as a Business Manager at the Postal Savings Bank of China. He then transitioned to academic research while pursuing his Ph.D., contributing to key projects at the Guizhou Key Laboratory of Information and Computing Science. In industry, Zhang served as a Senior R&D Engineer and later General Manager of the R&D Department at Guizhou-Cloud Big Data Industry Development Co. Since 2022, he has been an associate professor at Guizhou University of Finance and Economics and a postdoctoral researcher, continuing to innovate in computer science and data science fields.

πŸ…  Awards and Honors

Hui Zhang’s numerous recognitions include being a Review Expert for multiple academic and governmental bodies, such as the Guizhou Provincial Department of Science and Technology. His technical contributions have earned him industry accolades, and he was invited to join the Big Data Expert Committee of the China Computer Federation. Zhang has also been honored as an Industrial Mentor in Guizhou Province and contributed as an expert reviewer for the prestigious Alexandria Engineering Journal. His expertise in computational mathematics and data science has made him a sought-after advisor and collaborator.

🌍  Research Focus

Hui Zhang’s research focuses on computational mathematics, big data, and information systems, particularly in developing algorithms and systems for data processing and analysis. He has worked extensively on Pulsar Data Processing, contributing to the design and implementation of comparative analysis and visualization systems. His research extends into numerical analysis, with a focus on finite element methods for solving complex mathematical problems. Zhang’s interdisciplinary approach combines theoretical mathematics with practical applications in data science, making significant advances in these fields.

πŸ“– Publication Top Notes

  1. Generalized picture fuzzy Frank aggregation operators and their applications
  2. A second-order accurate and unconditionally energy stable numerical scheme for nonlinear sine-Gordon equation
  3. Asymmetrical interactions driven by strategic persistence effectively alleviate social dilemmas
  4. A Certificateless Verifiable Bilinear Pair-Free Conjunctive Keyword Search Encryption Scheme for IoMT
  5. Deformations and Extensions of Modified Ξ»-Differential 3-Lie Algebras

Dr. Soheila Kookalani | Construction | Best Researcher Award

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Dr. Soheila Kookalani | Construction | Best Researcher Award

Dr. Soheila Kookalani, Cambridge University, United Kingdom

Soheila Kookalani is a distinguished Research Associate at the University of Cambridge, specializing in Civil and Structural Engineering. She has a profound expertise in steel reuse, the circular economy, life-cycle assessment, and the integration of digital twin technologies in construction. Her research focuses on leveraging artificial intelligence and machine learning to optimize structural designs, promoting sustainability in construction. Soheila holds a Ph.D. in Civil and Structural Engineering from Shanghai Jiao Tong University, where she pioneered research on GFRP elastic gridshells. With a commitment to environmental responsibility, she continuously explores innovative ways to integrate sustainable practices into building designs, aiming to revolutionize construction methodologies. She has numerous publications in leading journals, demonstrating her contribution to both academia and the industry. Her work emphasizes sustainable engineering practices that align with modern technological advancements, particularly in the realm of structural optimization and reuse strategies.

Professional Profile

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Summary of Suitability for the Research for Best Researcher Award

Soheila Kookalani’s innovative approach to structural engineering, coupled with her strong commitment to sustainability and integration of advanced technologies, positions her as a highly suitable candidate for the Research for Best Researcher Award. Her work has significant implications for the future of sustainable construction, making her a valuable asset to both academia and industry.

πŸŽ“ Education 

Soheila Kookalani pursued her academic journey with a solid foundation in architectural and civil engineering. She earned her Bachelor of Science in Architectural Engineering from Azad University in Iran, where her thesis explored hybrid architecture in cinematic arts. She then advanced her studies with a Master of Science in Civil and Structural Engineering from Hohai University, China, focusing on the seismic performance of hybrid structures in high-rise buildings. Her academic journey culminated with a Ph.D. from Shanghai Jiao Tong University, China, where she focused on the structural optimization of GFRP elastic gridshells using machine learning techniques. Her doctoral research contributed to advancing knowledge in structural design and building sustainability. Throughout her studies, Soheila has consistently integrated innovative technologies, such as artificial intelligence and machine learning, into her research, making her an authority in the field of structural design and optimization for sustainable construction.

πŸ’Ό Experience 

Soheila Kookalani has accumulated extensive experience in civil and structural engineering, with a primary focus on sustainable design practices. She currently serves as a Research Associate at the University of Cambridge, where she is engaged in a groundbreaking project on the reuse of structural steel in construction. Her experience spans across diverse areas, including the circular economy, life-cycle assessment, building information modeling (BIM), and digital twin technology. From 2018 to 2022, she conducted research on GFRP elastic gridshells as part of her Ph.D. at Shanghai Jiao Tong University. Prior to this, Soheila gained practical experience working on seismic performance evaluation of hybrid structures during her Master’s at Hohai University. Her expertise also extends to the application of artificial intelligence in optimizing structural designs, demonstrating her capacity to bridge the gap between theoretical research and practical application in sustainable construction.

πŸ…Awards and Honors 

Throughout her academic and professional career, Soheila Kookalani has been recognized for her outstanding contributions to civil and structural engineering. She has received several prestigious awards and honors, including recognition from leading engineering conferences and academic institutions. Her work on the integration of AI and machine learning in structural design has garnered international attention, earning her accolades for innovation in sustainable construction. Soheila has also been honored for her research on GFRP elastic gridshells, receiving commendations for excellence in structural optimization and sustainable design. Her role as a published author in top-tier engineering journals has further solidified her reputation as a leading researcher in the field. Additionally, Soheila’s contributions to the reuse of structural steel and her involvement in cutting-edge projects at the University of Cambridge have earned her numerous industry awards, highlighting her commitment to environmental responsibility and sustainable engineering practices.

🌍 Research Focus 

Soheila Kookalani’s research is centered on the intersection of civil engineering, sustainability, and advanced technology. Her primary focus is on steel reuse, promoting a circular economy in construction through the reuse of structural components. She is also deeply involved in life-cycle assessments, aiming to reduce the environmental impact of construction projects. Her research integrates building information modeling (BIM) and digital twin technology to enhance the design, monitoring, and optimization of construction projects. Soheila is particularly interested in applying artificial intelligence (AI) and machine learning to optimize structural designs, especially in the context of GFRP elastic gridshells. Her work on generative AI in structural engineering seeks to streamline design processes and improve sustainability. By combining these advanced technologies, her research contributes to developing more efficient, eco-friendly building practices that align with global sustainability goals.

πŸ“– Publication Top Notes

  • BIM-based augmented reality for facility maintenance management
    • Cited by: 24
  • Structural analysis of GFRP elastic gridshell structures by particle swarm optimization and least square support vector machine algorithms
    • Cited by: 18
  • Shape optimization of GFRP elastic gridshells by the weighted Lagrange Ξ΅-twin support vector machine and multi-objective particle swarm optimization algorithm considering …
    • Cited by: 14
  • An analytic approach to predict the shape and internal forces of barrel vault elastic gridshells during lifting construction
    • Cited by: 14
  • Effect of Fluid Viscous Damper parameters on the seismic performance
    • Cited by: 14