Farrukh Dekhkonov | Computational Theory | Best Researcher Award

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Assoc. Prof. Dr. Farrukh Dekhkonov | Computational Theory | Best Researcher Award

Associate Professor |Namangan State University | Uzbekistan

Associate Professor Farrukh Dekhkonov is a researcher in the field of mathematical physics, focusing on differential equations, partial differential equations, applied mathematics, integral equations, boundary value problems, control theory, and mathematical modeling. His work emphasizes the development and analysis of boundary and time-optimal control problems associated with parabolic and pseudo-parabolic equations, including multi-dimensional domains and systems with involution. By investigating the heating process of rods, plates, and other physical systems, Dekhkonov has contributed significantly to understanding the dynamics of thermal processes and optimal control strategies in various geometries and boundary conditions. His research integrates theoretical analysis with computational methods to design effective control protocols for complex systems, ensuring precise management of heat transfer and diffusion processes. Through rigorous mathematical modeling, he has addressed both classical and novel control challenges, offering solutions for one-dimensional, two-dimensional, and three-dimensional equations of parabolic and pseudo-parabolic types, including periodic and involution-influenced systems. Dekhkonov’s work encompasses time-optimal control, establishing frameworks that minimize the time required to achieve desired system states while adhering to boundary constraints, and extends to the analysis of higher-order parabolic equations. Collaborative efforts have expanded the scope of his research, applying advanced techniques to multi-dimensional and higher-order systems, ensuring the robustness of control strategies across diverse physical models. Contributions to journals such as Discrete and Continuous Dynamical Systems, Communications in Analysis and Mechanics, and Lobachevskii Journal of Mathematics highlight the combination of theoretical innovation and practical application in his research. His investigations provide a foundation for future studies in mathematical control theory, emphasizing efficiency, stability, and adaptability in complex dynamic systems. Farrukh Dekhkonov’s expertise bridges abstract mathematical concepts with applied problem-solving, facilitating advancements in the precise manipulation of thermal and diffusion processes. 126 Citations, 23 Documents, 9 h-index, View h-index

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Dekhkonov, F. N., & Kuchkorov, E. I. (2023). On the time-optimal control problem associated with the heating process of a thin rod. Lobachevskii Journal of Mathematics, 44(3), 1134–1144. Cited by 19

Dekhkonov, F. (2022). On a time-optimal control of thermal processes in a boundary value problem. Lobachevskii Journal of Mathematics, 43(1), 192–198. Cited by 17

Дехконов, Ф. Н. (2022). On the control problem associated with the heating process. Математические заметки СВФУ, 29(4), 62–71. Cited by 15

Dekhkonov, F. N. (2023). Boundary control problem for the heat transfer equation associated with heating process of a rod. Bulletin of the Karaganda University. Mathematics Series, 110(2), 63–71. Cited by 14

Dekhkonov, F. (2023). On a boundary control problem for a pseudo-parabolic equation. Communications in Analysis and Mechanics, 15(2), 289–299. Cited by14

Dekhkonov, F. N. (2024). On the control problem associated with a pseudo-parabolic type equation in an one-dimensional domain. International Journal of Applied Mathematics, 37(1), 109–118. Cited by 11

Rana Ghazali | Data Science | Best Researcher Award

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Dr. Rana Ghazali | Data Science | Best Researcher Award

Researcher |McMaster University | Iran

Dr. Rana Ghazali focuses on advancing intelligent computing systems through the integration of machine learning, reinforcement learning, and large language models to optimize big data and distributed computing environments. Her work bridges the domains of cloud computing, Hadoop-based systems, and intelligent caching to enhance computational performance and resource utilization in large-scale data frameworks. She has contributed to innovative algorithms such as CLQLMRS and H-SVM-LRU for improving cache locality and intelligent cache replacement in MapReduce job scheduling, combining machine learning with distributed system optimization. Rana’s research also extends to the design and analysis of routing protocols in mobile ad hoc networks, leveraging bio-inspired algorithms such as the Ant Colony Optimization method to improve network efficiency. Her current exploration includes the application of reinforcement learning in scheduling and performance enhancement for distributed computing platforms, with additional attention to emerging paradigms like edge, fog, and serverless computing. As a researcher affiliated with the Resource Allocation and Stochastic Systems Lab at McMaster University, she contributes to cutting-edge discussions on adaptive data management, cyber and network security, and intelligent system design. Rana’s expertise further encompasses data analytics, large language models, and the intersection of artificial intelligence with real-world computing challenges. She has served as a reviewer for leading international journals including Elsevier and Wiley publications and has participated in academic collaborations that explore deep learning and resource optimization in distributed architectures. Her research endeavors consistently emphasize scalable, secure, and intelligent computational systems that advance the performance of modern data-intensive applications. Rana Ghazali has 13 citations, 2 documents, and an h-index of 2.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Ghazali, R., Down, D. G. (2025). Smart data prefetching using KNN to improve Hadoop performance. EAI Endorsed Transactions on Scalable Information Systems, 12(3). Cited by 1

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2023). Hadoop-oriented SVM-LRU (H-SVM-LRU): An intelligent cache replacement algorithm to improve MapReduce performance. arXiv preprint arXiv:2309.16471. Cited by 2

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2022). CLQLMRS: Improving cache locality in MapReduce job scheduler using Q-learning. Journal of Cloud Computing, 9. Cited by 9

Ghazali, R., Adabi, S., Down, D. G., & Movaghar, A. (2021). A classification of Hadoop job schedulers based on performance optimization approaches. Cluster Computing, 24(4), 3381–3403. Cited by 11

Ghazali, R., Down, D. G. (2025). A systematic overview of caching mechanisms to improve Hadoop performance. Concurrency and Computation: Practice and Experience, 37(25–26), e70337.

Samira Rahim | Computational Finance | Best Researcher Award

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Assist. Prof. Dr. Samira Rahim | Computational Finance | Best Researcher Award

Assist. Prof. Dr | University of Sciences and Technology Houari Boumediene | Algeria

Samira Rahim specializes in the field of stochastic methods, statistical modeling, and time series analysis, with a strong focus on the development and application of advanced probabilistic techniques for real-world data interpretation. Her research explores complex stochastic models, particularly in financial and meteorological contexts, where uncertainty and periodicity play critical roles. Through her work on periodic GARCH and threshold GARCH models, Rahim has contributed to improving the understanding of volatility dynamics and the statistical behavior of financial series with cyclical characteristics. Her investigations emphasize the importance of model mixtures and the application of local asymptotic normality principles, offering deeper insights into how stochastic processes can better capture temporal dependencies in financial and environmental data. With a background in statistics and operational research, Rahim integrates both theoretical and applied perspectives to construct robust predictive and inferential frameworks. Her expertise extends to stochastic optimization and probabilistic analysis, contributing to the development of more adaptive forecasting tools for risk management and decision-making under uncertainty. Her academic contributions, including her publication in Communications in Statistics – Theory and Methods in 2025, demonstrate her commitment to advancing research in the mathematical and statistical sciences. Rahim’s long-term experience in academia and her engagement with research projects underscore her dedication to innovation in stochastic modeling and data-driven approaches. Her work aligns with contemporary challenges in data science, emphasizing the critical role of statistical methodologies in improving predictive accuracy and understanding complex systems governed by random processes. Samira Rahim’s research continues to strengthen the intersection of probability theory, operational research, and applied statistics, contributing significantly to both theoretical advancements and their practical implementations in diverse scientific domains.

Profiles: Google Scholar
Featured Publication

Rahim, S., & Guerbyenne, H. (2025). Local asymptotic normality in periodic threshold GARCH and periodic GARCH models. Communications in Statistics – Theory and Methods, 1–20.

Ayman Al Hayek | Education Technology | Best Researcher Award

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Dr. Ayman Al Hayek | Education Technology | Best Researcher Award

Diabetes Educator| Prince Sultan Military Medical City | Saudi Arabia

Dr. Ibrahim A. Al-Ghofaili is a distinguished medical professional specializing in family medicine, diabetes, and obesity management, with extensive clinical, research, and leadership experience across Saudi Arabia’s premier medical institutions. His professional journey includes key roles at Prince Sultan Military Medical City and the Ministry of Defense, progressing from family medicine registrar to consultant and head of the Diabetic Center. His research interests bridge preventive, metabolic, and endocrine health, emphasizing diabetes management, obesity medicine, and public health awareness. Al-Ghofaili’s scholarly work includes studies on the use of alternative medicine by cancer patients, prevalence of osteoporosis risk factors among young Saudi women, incidence and risk factors of hypoglycemia in chronic kidney disease, and factors influencing diabetic foot self-care efficacy. His active participation in international conferences such as the ADA Symposium, European Association for the Study of Diabetes (EASD), and Obesity Week highlights his dedication to advancing clinical knowledge and integrating global best practices into Saudi healthcare. Through various leadership and educational initiatives, he has contributed significantly to developing training frameworks and health service standards within the Saudi Commission for Health Specialties. His engagement in medical education, executive healthcare leadership programs, and obesity-focused fellowships underscores his multidisciplinary expertise in both patient-centered care and healthcare management. Ibrahim A. Al-Ghofaili’s research aims to enhance chronic disease prevention and management, aligning with national and international goals for improved metabolic health outcomes and lifestyle interventions. His continued academic involvement and clinical leadership position him as an influential figure in advancing family and lifestyle medicine, diabetes care, and obesity research in the Gulf region. Ibrahim A. Al-Ghofaili has achieved 1,193 citations, 48 documents, and an h-index of 18 according to the latest research metrics.

Profiles: Scopus | ORCID
Featured Publication

Al-Ghofaili, I. A., et al. (2025). Impact of transitioning from conventional blood glucose monitoring to continuous glucose monitoring on glycemic control and self-management in adults with type 2 diabetes on oral glucose-lowering medications. Diabetes Research and Clinical Practice.

Al-Ghofaili, I. A., et al. (2025). Evaluating the effect of Semaglutide as add-on therapy on glycemic control and continuous glucose monitoring outcomes in adults with type 1 diabetes: A two-year real-world data study. Journal of Diabetes and Its Complications.

Al-Ghofaili, I. A., et al. (2025). Evaluation of glycemia risk index and continuous glucose monitoring-derived metrics in type 1 diabetes: A real-world observational study. Journal of Diabetes and Metabolic Disorders.

Mitsuru Endo | Computational Theory | Best Researcher Award

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Prof. Dr. Mitsuru Endo | Computational Theory | Best Researcher Award

Professor Emeritus| Tokyo Institute of Technology | Japan

Mitsuru Endo has made distinguished contributions to applied mechanics and vibration engineering, focusing on the dynamic behavior of continua and structures and the development of advanced noise and vibration control systems. His work bridges theoretical mechanics and practical applications in acoustic control, offering innovative solutions for vibration reduction in engineering systems. Endo has pioneered the extension of Southwell-Dunkerley methods for synthesizing frequencies, contributing to a deeper understanding of vibrational modes in complex systems. His research on flexural vibrations of rotating rings and deformation theories for beams, plates, and cylindrical shells has advanced modeling precision in mechanical structures. By introducing alternative formulations for Timoshenko beam and Mindlin plate models, Endo improved computational accuracy in vibration analysis. His innovative “one-half order shear deformation theory” redefined how transverse shear deformation is represented in structural mechanics, influencing global research on elasticity and composite structures. Endo’s extensive publications in leading journals such as the Journal of Sound and Vibration and the International Journal of Mechanical Sciences have established a strong foundation for future explorations in vibration modeling, acoustic optimization, and structural mechanics. His studies integrate both analytical and experimental perspectives, driving advancements in passive and active noise control technologies essential to aerospace, automotive, and civil engineering applications. The recognition of his work through multiple prestigious awards underscores his impact in mechanical sciences and engineering research, with 440 citations, 64 documents, and an h-index of 8.

Profiles: Scopus | ORCID
Featured Publication

Endo, M. (2013). Study on direct sound reduction structure for reducing noise generated by vibrating solids. Journal of Sound and Vibration, 332, 2643–2658. 5 citations

Endo, M. (2015). Study on an alternative deformation concept for the Timoshenko beam and Mindlin plate models. International Journal of Engineering Science, 87, 32–56. 34 citations

Endo, M. (2016). An alternative first-order shear deformation concept and its application to beam, plate and cylindrical shell models. Composite Structures, 146, 50–61. 17 citations

Endo, M. (n.d.). Study on the characteristics of noise reduction effects of a sound reduction structure. Conference Paper. 1 citation

Fu Chaotian | Software Engineering | Best Researcher Award

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Dr. Fu Chaotian | Software Engineering | Best Researcher Award

Researcher | China Coal Technology and Engineering Group Shenyang Research Institute Co., Ltd. | China

Tian Fuchao, a distinguished researcher and chief scientist at the China Coal Technology and Engineering Group Shenyang Research Institute Co., Ltd., has established a remarkable career focusing on mine fire and gas disaster prevention under coupled conditions, industrial environmental gas spectroscopy, and advanced instrument development. His extensive research has led to the successful completion of 34 major research projects, the publication of 87 peer-reviewed journal papers, 5 books, and 69 patents, showcasing a strong integration of theory and industrial application. With an SCI h-index of 16, his work demonstrates both depth and impact within the field of coal mine safety and gas control technologies. Tian Fuchao has played a central role in developing state-of-the-art scientific instruments such as the TZX-1000 atmospheric environment monitoring system, TZX-3000 intelligent gas adsorption-desorption platform, and TZX-7000 laser-based multi-parameter detection system, which have been widely adopted across China. His research collaborations with the National Natural Science Foundation of China have focused on the mechanisms of gas desorption and spontaneous combustion in coal under multi-field coupling, contributing significantly to understanding disaster mechanisms in deep mining environments. Additionally, his editorial roles in Journal of China Coal Society and China Safety Science Journal reflect his leadership in advancing scientific communication. As a professional member of the China Occupational Safety and Health Association’s Youth Committee, Tian Fuchao continues to promote innovation and safety culture in industrial research. His ongoing efforts toward coupling analysis, predictive modeling, and intelligent monitoring technologies mark him as a leading figure in coal mine safety and environmental engineering. Tian Fuchao’s pioneering research continues to advance global understanding of energy safety and sustainable industrial operations, with 1,140 citations from 1,000 documents, 98 publications, and an h-index of 19.

Profiles:  Scopus | ORCID
Featured Publication

Thermal stability of acidified coal under coupled pressure and temperature effects. Thermal Science and Engineering Progress.

Explosion behavior of premixed LPG-hydrogen/air mixtures in a square duct. Physics of Fluids.

Dynamic viscosity evolution characteristics of ScCO₂ and its effects on carbon geological sequestration efficiency. Energy.

Precise gas control technology for fault fracture zones in deep roadways. Physics of Fluids.

Roof fracture dynamics and synergistic control strategies for mitigating coal mine gas disasters in goaf regions. Mining, Metallurgy and Exploration.

Arif Basgumus | Mobile Computing | Best Researcher Award

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Dr. Arif Basgumus | Mobile Computing | Best Researcher Award

Associate Professor | Bursa Uludag University | Turkey

Dr. Arif Basgumus is a distinguished Associate Professor at Bursa Uludag University, whose research profoundly advances wireless communication, signal processing, and next-generation network systems. His extensive contributions encompass cognitive radio networks, non-orthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RIS), cooperative communications, integrated sensing and communication (ISAC), and physical layer security. Dr. Arif Basgumus has developed robust models for interference alignment, hybrid RF/VLC systems, and UAV-assisted network architectures, contributing significantly to 5G and 6G technology evolution. His studies integrate theoretical modeling with artificial intelligence applications, enhancing the efficiency and reliability of communication frameworks. Actively collaborating with industrial partners such as ASELSAN, HAVELSAN, and TUSAŞ, he bridges academic innovation with practical defense and aerospace applications. His authorship spans influential journals including IEEE Access, IET Communications, and Digital Signal Processing, reflecting a consistent research impact in signal optimization, deep learning-aided communications, and security enhancement in RIS-assisted systems. He has guided numerous graduate theses, emphasizing interdisciplinary approaches across electrical, electronics, and computer engineering. His projects funded by TUBITAK and other research councils explore UAV communication, smart vehicle systems, and optical sensor networks, fostering sustainable and intelligent connectivity. Dr. Arif Basgumus has also co-authored several books and chapters on communication systems, cognitive networks, and artificial intelligence in engineering. His long-standing involvement in international collaborations and IEEE activities highlights a leadership role in shaping the technological foundations of future communication infrastructures, with 256 citations, 48 documents, and an h-index of 10 (View h-index).

Profiles:  Google Scholar | Scopus | ORCID
Featured Publication

Alakoca, H., Namdar, M., Aldirmaz-Colak, S., Basaran, M., & Basgumus, A. (2022). Metasurface manipulation attacks: Potential security threats of RIS-aided 6G communications. IEEE Communications Magazine, 61(1), 24–30. Citations: 43

Bayhan, E., Ozkan, Z., Namdar, M., & Basgumus, A. (2021). Deep learning-based object detection and recognition of unmanned aerial vehicles. In Proceedings of the 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications. Citations: 41

Ozkan, Z., Bayhan, E., Namdar, M., & Basgumus, A. (2021). Object detection and recognition of unmanned aerial vehicles using Raspberry Pi platform. In Proceedings of the 5th International Symposium on Multidisciplinary Studies and Innovative Technologies. Citations: 34

Altuncu, A., & Basgumus, A. (2005). Gain enhancement in L-band loop EDFA through C-band signal injection. IEEE Photonics Technology Letters, 17(7), 1402–1404. Citations: 27

Basgumus, A., Durak, F. E., Altuncu, A., & Yilmaz, G. (2015). A universal and stable all-fiber refractive index sensor system. IEEE Photonics Technology Letters, 28(2), 171–174. Citations: 26

Umakoglu, I., Namdar, M., Basgumus, A., Kara, F., Kaya, H., & Yanikomeroglu, H. (2021). BER performance comparison of AF and DF assisted relay selection schemes in cooperative NOMA systems. In Proceedings of the 2021 IEEE International Black Sea Conference on Communications and Networking. Citations: 22

Mustafa Namdar | High Performance Computing | Best Researcher Award

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Assoc. Prof. Dr . Mustafa Namdar | High Performance Computing | Best Researcher Award

Assoc. Prof. Dr | Kutahya Dumlupinar University| Turkey

Dr. Mustafa Namdar has established a distinguished research profile in advanced wireless communications and network technologies, focusing on cognitive radio networks, cooperative communications, relay networks, interference alignment, non-orthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISAC). His work emphasizes innovative solutions to enhance spectral efficiency, reliability, and performance in next-generation communication systems. Dr. Namdar has contributed extensively to the development of receiver diversity and dispersed spectrum sensing techniques, enabling efficient utilization of available spectrum and minimizing interference in dynamic network environments. His research integrates theoretical modeling with practical system design, addressing complex challenges in 5G and emerging 6G wireless technologies. He has actively collaborated on projects related to cooperative relay networks and interference management, which are critical for optimizing throughput and ensuring robust connectivity in dense network scenarios. In addition to his contributions to physical layer design, Dr. Namdar explores the potential of reconfigurable intelligent surfaces and ISAC frameworks to simultaneously support communication and sensing functionalities, offering a transformative approach for intelligent wireless networks. His work has been recognized through multiple awards, including the Outstanding Reviewer Award from Elsevier-AEU Journal and IEEE SIU Conference accolades, reflecting both the quality and impact of his research in the international community. Dr. Namdar’s expertise in NOMA and interference alignment provides practical solutions for multi-user communication scenarios, significantly advancing spectral efficiency and network capacity. He has also played a pivotal role in the technical evaluation of numerous research projects and has contributed as a reviewer and TPC member across prestigious IEEE conferences and journals, ensuring high standards in scholarly communications. His ongoing research aims to drive the evolution of future wireless networks with integrated sensing, enhanced resource allocation, and next-generation communication protocols. 272 Citations, 54 Documents, 10 h-index, View h-index.

Profiles:  Google Scholar | Scopus
Featured Publication

Alakoca, H., Namdar, M., Aldırmaz-Çolak, S., Basaran, M., Basgumus, A., & … (2023). Metasurface manipulation attacks: Potential security threats of RIS-aided 6G communications. IEEE Communications Magazine, 61(1), 24–30. 43 citations

Bayhan, E., Ozkan, Z., Namdar, M., & Basgumus, A. (2021). Deep learning based object detection and recognition of unmanned aerial vehicles. 2021 IEEE 3rd International Congress on Human-Computer Interaction. 41 citations

Ozkan, Z., Bayhan, E., Namdar, M., & Basgumus, A. (2021). Object detection and recognition of unmanned aerial vehicles using Raspberry Pi platform. 2021 IEEE 5th International Symposium on Multidisciplinary Studies and … 34 citations

Namdar, M., & Ilhan, H. (2018). Exact closed-form solution for detection probability in cognitive radio networks with switch-and-examine combining diversity. IEEE Transactions on Vehicular Technology, 67(9), 8215–8222. 23 citations

Namdar, M., Ilhan, H., & Durak-Ata, L. (2016). Optimal detection thresholds in spectrum sensing with receiver diversity. Wireless Personal Communications, 87, 63–81. 23 citations

Umakoglu, I., Namdar, M., Basgumus, A., Kara, F., Kaya, H., & Yanikomeroglu, H. (2021). BER performance comparison of AF and DF assisted relay selection schemes in cooperative NOMA systems. 2021 IEEE International Black Sea Conference on Communications and … 22 citations

Caitlin Kent | Open Source | Best Researcher Award

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Ms. Caitlin Kent | Open Source | Best Researcher Award

Honours Student | University of Wollongong | Australia

Ms. Caitlin Kent has built a distinguished career in nursing and health research, with a particular focus on global and tropical health, immunisation programs, and community-based research. Her role at the Menzies School of Health Research demonstrates expertise in coordinating participant recruitment, conducting mixed-methods and qualitative research, and presenting findings to stakeholders and policy partners. Caitlin’s work contributes to the advancement of public health initiatives aimed at addressing disease prevention and improving healthcare outcomes in diverse populations. Her professional experience spans both clinical and research settings, including emergency departments, acute care, and neurological wards, where she has developed critical skills in patient management, leadership, and interdisciplinary collaboration. As part of the “B Part of it NT” Meningococcal B vaccine study, Caitlin engaged in public health research focused on vaccination coverage and disease control in Northern Territory communities. Her work as a Nurse Immuniser during the Covid-19 and Influenza vaccination rollout further reflects her dedication to preventive healthcare and rural outreach. In addition to her clinical expertise, Caitlin’s engagement in leadership and mentorship programs—such as the Menzies Leadership Development Program, Emerging Research Leadership Program, and Catalyse Mentorship Program—highlights her commitment to advancing research capacity and promoting women in STEMM. Her recognition through academic awards and participation in professional development programs underscores a strong foundation in evidence-based practice and applied health research. Through her ongoing Honours in Nursing and active involvement in multidisciplinary research initiatives, Caitlin continues to contribute to improving healthcare delivery, fostering community health awareness, and strengthening the integration of clinical practice with research innovation across Australia’s tropical and remote health landscape.

Profile:  ORCID
Featured Publication

Kent, C., & Halcomb, E. (2025). Nurses’ experiences of mental health care in the emergency department: An integrative review. Journal of Clinical Nursing, 34(9–10), 1–15.

Ying Yi Tan | Smart Manufacturing | Best Researcher Award

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Dr. Ying Yi Tan | Smart Manufacturing | Best Researcher Award

Research Fellow | Singapore University of Technology and Design | Singapore

Dr. Tan Ying Yi is a Research Fellow at the Singapore University of Technology and Design (SUTD) whose research lies at the intersection of digital fabrication, smart textiles, and computational design. The focus of his work is the development of digital knitting technologies and multi-material additive manufacturing methods for creating functional, mechanically graded, and intelligent textile-based systems. His investigations explore how knitted fabrics can be engineered with integrated electrical and mechanical properties, transforming traditional textiles into high-performance materials applicable to both architectural and biomedical domains. Ying Yi has played a significant role in advancing customized technical textiles for applications such as structural membranes, façade systems, prosthetic interfaces, and wearable technologies. His leadership in projects involving smart garments for body joint monitoring has contributed to innovations in digital health and human–machine interaction, demonstrating the potential of computational design and materials research to improve quality of life. Collaborative projects with institutions like SingHealth Polyclinics, Tan Tock Seng General Hospital, and Hyundai Motor Group have led to impactful real-world solutions such as smart knee braces for gait assessment and smart shirts for motion tracking. His work is characterized by an interdisciplinary approach, blending engineering precision, material science, and architectural design principles to create responsive systems that interact dynamically with users and environments. Recognized with awards for excellence in architectural membranes and advanced manufacturing, Ying Yi continues to contribute to the integration of digital fabrication, computational modeling, and soft robotics in technical textile research. His studies have been featured by major media outlets for their innovation and societal relevance, showcasing how fabric-based systems can bridge the gap between engineering and design. Citations 19 Documents 5 h-index View.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Weeger, O., Sakhaei, A. H., Tan, Y. Y., Quek, Y. H., Lee, T. L., Yeung, S. K., & Kaijima, S. (2018). Nonlinear multi-scale modelling, simulation and validation of 3D knitted textiles. Applied Composite Materials, 25(4), 797–810. Citations: 43

Sakhaei, A. H., Kaijima, S., Lee, T. L., Tan, Y. Y., & Dunn, M. L. (2018). Design and investigation of a multi-material compliant ratchet-like mechanism. Mechanism and Machine Theory, 121, 184–197. Citations: 31

Gupta, S. S., Tan, Y. Y., Chia, P. Z., Pambudi, C. P., Quek, Y. H., Yogiaman, C., & Tracy, K. J. (2020). Prototyping knit tensegrity shells: A design-to-fabrication workflow. SN Applied Sciences, 2(6), 1062. Citations: 25

Do, H., Tan, Y. Y., Ramos, N., Kiendl, J., & Weeger, O. (2020). Nonlinear isogeometric multiscale simulation for design and fabrication of functionally graded knitted textiles. Composites Part B: Engineering, 202, 108416. Citations: 20

Gupta, U., Lau, J. L., Chia, P. Z., Tan, Y. Y., Ahmed, A., Tan, N. C., Soh, G. S., & Low, H. Y. (2023). All knitted and integrated soft wearable of high stretchability and sensitivity for continuous monitoring of human joint motion. Advanced Healthcare Materials, 12(21), 2202987. Citations: 17

Pal, A., Chan, W. L., Tan, Y. Y., Chia, P. Z., & Tracy, K. J. (2020). Knit concrete formwork. Proceedings of the 25th CAADRIA Conference, 1, 213–222. Citations: 7