Kevin Patel | Engineering | Best Industrial Research Award

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Mr. Kevin Patel | Engineering | Best Industrial Research Award

Quality Engineer – Lead | Futaba North America Engineering & Marketing Corp | United States

Kevin Patel is a research-driven quality innovation leader with over a decade of expertise in advanced automotive systems, high-reliability electronics, and cyber-physical manufacturing ecosystems, with a strong focus on AI-enhanced diagnostics, robotic automation, IoT-integrated quality informatics, and adaptive process control for next-generation industrial engineering. He earned his Bachelor of Engineering in Mechanical Engineering from Gujarat Technological University in 2017, followed by a Master of Engineering in Mechanical Engineering from the Illinois Institute of Technology, Chicago, in 2019, where he strengthened his foundations in design, manufacturing, and applied research. Professionally, he has contributed significantly across leading organizations including Futaba North America, Electronic Interconnect, Machined Products Co., and Hindustan Door Oliver, where he led initiatives in AI-driven defect detection, predictive scrap reduction, supplier quality systems, lean manufacturing, and digital process optimization. His research interests lie in the integration of artificial intelligence, IoT, and blockchain for predictive quality, defect prevention, and smart manufacturing ecosystems, with publications in reputed and indexed platforms covering topics such as self-healing production lines, generative quality networks, PCB optimization, and autonomous supplier ecosystems. He possesses strong research and technical skills including CAD and CAE tools (AutoCAD, SolidWorks, CATIA, Abaqus, Ansys), manufacturing quality tools (SPC, CAPA, DoE, DMAIC, Kaizen, GD&T), and advanced industry standards (ISO 9001, IATF 16949, AS9100). His certifications include IATF 16949/ISO 9001 Lead Auditor, Lean Six Sigma Green Belt, SolidWorks CSWA, and Autodesk AutoCAD Professional, reflecting his commitment to continuous professional growth. Kevin has also been recognized for his ability to bridge academic research with industrial application, authoring multiple Scopus and IEEE-indexed papers that demonstrate global relevance. With his multidisciplinary expertise, impactful publications, leadership roles, and future potential in advancing intelligent manufacturing, Kevin Patel stands out as an outstanding candidate for recognition, embodying both academic rigor and industrial innovation.

Profile: ORCID

Featured Publications

  1. Patel, K. (2025). AI-driven defect detection in PCB manufacturing: A computer vision approach using convolutional neural networks. European Journal of Advances in Engineering and Technology.

  2. Patel, K. (2025, July 29). AI+IoT+Blockchain triad for smart traceability in the automotive industry. International Journal of Research and Scientific Innovation.

  3. Patel, K. (2025, June 1). Agentic AI for self-healing production lines: Autonomous root cause analysis & correction. Journal of Information Systems Engineering and Management.

  4. Patel, K. (2025, June 1). Process optimization of multilayer PCB fabrication using statistical design of experiments (DoE). Journal of Information Systems Engineering and Management.

  5. Patel, K. (2025, April 12). Generative quality networks (GQNs): Leveraging GenAI to predict unprecedented defects in automotive manufacturing. International Journal of Science and Research (IJSR).

Soheila Kookalani | Engineering | Best Paper Awards

Published on by Best Paper Awards

Dr. Soheila Kookalani | Engineering | Best Paper Award

Research Associate at Cambridge University, United Kingdom

Dr. Soheila Kookalani is a distinguished researcher in civil and structural engineering, specializing in artificial intelligence, machine learning, sustainable construction, and digital twin technologies. Currently a Research Associate at the University of Cambridge, her work focuses on steel reuse, circular economy, and structural optimization to promote sustainable infrastructure. She earned her Ph.D. in Civil and Structural Engineering from Shanghai Jiao Tong University, supported by strong academic training at Hohai University and Azad University. With an impressive publication record in high-impact journals and international conferences, she has advanced knowledge in structural design automation and resilient construction practices. Beyond research, she contributes as a reviewer, editorial board member, guest editor, and invited speaker, while also teaching and mentoring students. Her achievements demonstrate academic excellence, global collaboration, and leadership in advancing sustainable engineering solutions.

Professional Profile

Google Scholar | Orcid Profile | Scopus Profile

Education

Dr. Soheila Kookalani has built a strong academic foundation across globally recognized institutions. She earned her Ph.D. in Civil and Structural Engineering from Shanghai Jiao Tong University, where her research explored structural optimization and machine learning applications for gridshell structures. She completed her Master’s degree in Civil and Structural Engineering at Hohai University, focusing on the seismic performance of steel-concrete hybrid structures, following a Bachelor’s in Architectural Engineering from Azad University, where she developed hybrid architecture concepts for sustainable design. This academic journey provided her with multidisciplinary expertise spanning architecture, civil engineering, and computational modeling. Her progression from undergraduate through doctoral studies highlights a consistent dedication to merging innovative design with engineering principles, forming the basis for her later research on sustainable construction, digital twins, and artificial intelligence-driven structural design.

 Experience

Dr. Kookalani is currently a Research Associate in Construction Engineering at the University of Cambridge, where she leads work on sustainable construction practices, including steel reuse, circular economy applications, and digital twin technologies. Her role has involved collaboration with international partners and industry stakeholders to develop innovative solutions for life cycle assessment and sustainable design. She has actively contributed to teaching and supervision at Cambridge, engaging with undergraduate and postgraduate students in mechanics, aerodynamics, and structural engineering courses. Previously, she undertook significant academic and research roles during her studies in China, working on advanced computational and structural analysis projects. Her professional experience is distinguished by its combination of high-impact research, curriculum development, knowledge transfer, and industry collaboration, positioning her as a bridge between academic innovation and practical engineering applications.

Research Interest

Dr. Kookalani’s research interests are centered on sustainable structural engineering and the integration of advanced technologies into civil infrastructure. She focuses on steel reuse, structural optimization, circular economy approaches, and life cycle assessment to advance sustainable design practices. Her expertise in artificial intelligence, machine learning, and deep learning enables her to apply advanced computational models to construction automation, lightweight structures, and generative design. She is also deeply engaged in digital twin applications, building information modeling, and robotics for the built environment, reflecting a forward-looking vision for smart and adaptive construction systems. Her interdisciplinary approach connects materials science, computational engineering, and sustainability, making her research highly relevant for addressing global challenges in resource efficiency, climate change mitigation, and infrastructure resilience.

Awards and Honors

Dr. Kookalani has earned multiple academic honors in recognition of her scholarly excellence and dedication. She received a full scholarship from Shanghai Jiao Tong University to pursue her Ph.D., reflecting her strong academic merit and research potential. Prior to that, she was awarded a scholarship for her Master’s studies at Hohai University. During her undergraduate years at Azad University, she was consistently recognized as a top student in architectural design courses, with her projects highlighted for their creativity and development. She also served as a member of the student board at the Architecture Engineering Scientific Association, demonstrating early leadership and academic engagement. These achievements reflect a trajectory of sustained academic distinction, research innovation, and leadership, laying a strong foundation for her ongoing success as a global researcher in sustainable engineering.

Research Skill

Dr. Kookalani possesses a comprehensive set of research skills that combine computational expertise, engineering knowledge, and interdisciplinary applications. She is proficient in programming languages such as Python and MATLAB, and advanced software including Abaqus, AutoCAD, Revit, Rhino, Grasshopper, Etabs, and SAP2000, enabling her to model, analyze, and optimize complex structures. Her technical expertise extends to machine learning, digital twins, life cycle assessment, and environmental product declarations, aligning with her sustainability-focused research. She is adept in data-driven modeling, structural performance prediction, and optimization techniques such as swarm intelligence and support vector machines. In addition, she has experience in visualization tools like Lumion, Blender, and Adobe Suite, enhancing her ability to present research outputs effectively. These skills empower her to bridge advanced computational methods with practical engineering solutions for sustainable construction.

Publication Top Notes

Title: Trajectory of Building and Structural Design Automation from Generative Design Towards the Integration of Deep Generative Models and Optimization: A Review
Authors: Soheila Kookalani, E. Parn, I. Brilakis, S. Dirar, M. Theofanous, A. Faramarzi, M. Mahdavipour, Q. Feng
Year: 2024
Citation: Journal of Building Engineering, 97:110972

Title: Shape Optimization of GFRP Elastic Gridshells by the Weighted Lagrange Ε-Twin Support Vector Machine and Multi-Objective Particle Swarm Optimization Algorithm Considering Structural Weight
Authors: Soheila Kookalani, B. Cheng, S. Xiang
Year: 2021
Citation: Structures, 33:2066–2084

Title: Structural Performance Assessment of GFRP Elastic Gridshells by Machine Learning Interpretability Methods
Authors: Soheila Kookalani, B. Cheng, J. L. Chavez Torres
Year: 2022
Citation: Frontiers of Structural and Civil Engineering, 16:1249–1266

Title: Form-Finding of Lifting Self-Forming GFRP Elastic Gridshells Based on Machine Learning Interpretability Methods
Authors: Soheila Kookalani, S. Nyunn, S. Xiang
Year: 2022
Citation: Structural Engineering and Mechanics, 84(5):605–618

Title: An Overview of Optimal Damper Placement Methods in Structures
Authors: Soheila Kookalani, D. Shen, L. Zhu, M. Lindsey
Year: 2021
Citation: Iranian Journal of Science and Technology – Transactions of Civil Engineering, 46:1785–1804

Title: An Analytic Solution for Form Finding of GFRP Elastic Gridshells during Lifting Construction
Authors: S. Xiang, B. Cheng, Soheila Kookalani
Year: 2020
Citation: Composite Structures, 244:112290

Title: An Analytic Approach to Predict the Shape and Internal Forces of Barrel Vault Elastic Gridshells during Lifting Construction
Authors: S. Xiang, B. Cheng, Soheila Kookalani, J. Zhao
Year: 2021
Citation: Structures, 29:628–637

Title: An Integrated Approach of Form Finding and Construction Simulation for Glass Fiber-Reinforced Polymer Elastic Gridshells
Authors: S. Xiang, B. Cheng, L. Zou, Soheila Kookalani
Year: 2020
Citation: Structural Design of Tall and Special Buildings, 29(5):e1698

Title: Introduction of Methodology for BIM & DSS
Authors: H. Alavi, Soheila Kookalani, F. Rahimian, N. Forcada
Year: 2024
Citation: Integrated Building Intelligence, pp. 31–42

Title: BIM-Based DSS for HVAC Root-Cause Detection
Authors: H. Alavi, Soheila Kookalani, F. Rahimian, N. Forcada
Year: 2024
Citation: Integrated Building Intelligence, pp. 43–57

Title: BIM-Based DSS for Building Condition Assessment
Authors: H. Alavi, Soheila Kookalani, F. Rahimian, N. Forcada
Year: 2024
Citation: Integrated Building Intelligence, pp. 59–78

Title: BIM-Based DSS for Enhancing Occupants’ Comfort
Authors: H. Alavi, Soheila Kookalani, F. Rahimian, N. Forcada
Year: 2024
Citation: Integrated Building Intelligence, pp. 79–99

Title: BIM-Based Augmented Reality for Facility Maintenance Management
Authors: H. Alavi, Soheila Kookalani, F. Rahimian, N. Forcada
Year: 2024
Citation: Integrated Building Intelligence, pp. 101–112

Title: GFRP Elastic Gridshell Structures: A Review of Methods, Research, Applications, Opportunities, and Challenges
Authors: Soheila Kookalani, Htay Htayaung
Year: 2023
Citation: Journal of Civil Engineering and Materials Application

Title: Structural Analysis of GFRP Elastic Gridshell Structures by Particle Swarm Optimization and Least Square Support Vector Machine Algorithms
Authors: Soheila Kookalani, B. Cheng
Year: 2021
Citation: Journal of Civil Engineering and Materials Application

Title: Effect of Fluid Viscous Damper Parameters on the Seismic Performance
Authors: Soheila Kookalani, D. Shen
Year: 2020
Citation: Journal of Civil Engineering and Materials Application, 4(3)

Title: An Overview of the Particle Swarm Optimization Algorithms Applied to Optimization of Structures
Authors: Soheila Kookalani
Year: 2019
Citation: Civil Engineering Journal, 5(11):2336–2349

Title: Analysis and Optimal Location of Fluid Viscous Dampers for Multistory Irregular Steel Structures under Seismic Excitation
Authors: Soheila Kookalani, M. Daneshvaran, M. Noori
Year: 2019
Citation: Civil Engineering Journal, 5(7):1594–1607

Title: Optimal Viscous Damper Location for Multi-Story Steel Structures by Genetic Algorithm
Authors: Soheila Kookalani, S. Arabzadeh, M. Noori
Year: 2018
Citation: Civil Engineering Journal, 4(11):2590–2601

Title: Optimal Placement of Fluid Viscous Dampers in Steel Structures Subjected to Seismic Excitation by Genetic Algorithm
Authors: Soheila Kookalani, S. Arabzadeh, M. Noori
Year: 2018
Citation: Civil Engineering Journal, 4(5):1061–1072

Conclusion

Dr. Soheila Kookalani is an innovative and forward-thinking researcher whose career integrates civil engineering, artificial intelligence, and sustainability. With strong academic credentials, professional experience at leading institutions, and a significant publication record, she has made meaningful contributions to the advancement of structural optimization, digital construction, and sustainable design. Her work has influenced both academia and industry by offering scalable solutions for steel reuse, resilient infrastructure, and circular economy practices. Beyond research, her leadership through teaching, editorial activities, conference committees, and invited talks reflects her commitment to knowledge sharing and community impact. Recognized with prestigious scholarships and awards, she continues to expand her global collaborations and research impact. Dr. Kookalani exemplifies academic excellence, technical innovation, and societal contribution, making her a valuable contributor to the future of sustainable engineering.