Vikas Mehta | Statistical Computing and Programming | Research Excellence Award

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Dr. Vikas Mehta | Statistical Computing and Programming | Research Excellence Award

Korean National Institute for International Education | South Korea

Dr. Vikas Mehta is a structural engineer and researcher specializing in seismic performance optimization, sustainable construction materials, and the application of advanced computational and machine learning methodologies to civil infrastructure systems. He completed his Ph.D. in Civil Engineering at Keimyung University, South Korea, where his award-winning doctoral research introduced innovative modifier-based and data-driven techniques for improving shear strength prediction and design accuracy in reinforced concrete beam-column joints. His expertise spans nonlinear finite element modeling, fragility analysis, physics-informed and graph-based machine learning, geospatial analytics, and performance-based seismic assessment, supported by strong proficiency in ETABS, OpenSees, SeismoSoft, Abaqus, MATLAB, Q-GIS, SPSS, Python, PyTorch, WEKA, and OriginPro. Dr. Mehta serves as a Postdoctoral Researcher at the Chonnam National University R&BD Foundation, contributing to advanced safety technologies for nuclear power plant structures under extreme hazard scenarios, including buckling resistance enhancement, retrofit optimization, and complex wind–terrain interaction studies. His professional background includes academic appointments in structural and construction engineering, where he taught subjects in earthquake engineering, finite element analysis, and structural systems while supervising graduate research and contributing to curriculum and laboratory development. Dr. Mehta has authored a substantial body of SCI-indexed research on seismic damage prediction, torsional behavior modeling, hybrid AI-mechanics frameworks, recycled and sustainable materials, computational methods, and structural performance evaluation, complemented by multiple patents in construction materials, damping devices, and waste-based composites. He has presented at leading international and national conferences and contributed to funded collaborative research, including projects involving global academic and industry partners. His professional affiliations include membership in ASCE, the Institute of Physics (AMInstP), IAEME (Fellow), and licensure as a Class-A engineer under the Himachal Pradesh Town and Country Planning Act. Dr. Mehta’s contributions to structural engineering and computational mechanics continue to gain international visibility, reflected in an h-index of 7, over 172 citations, and more than 19 published documents, underscoring his growing influence in machine learning–driven structural design, seismic resilience, and sustainable construction innovation.

Profiles: Scopus | Orcid

Featured Publications

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Corrigendum to “Adaptive simulation and data-driven hybrid modeling for predicting shear strength and failure modes of interior reinforced concrete beam-column joints”.

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Predictive framework for shear strength and failure modes of exterior reinforced concrete beam–column joints using machine learning. Structural Concrete. h.

Sagar, G. S., Mukthi, S., & Mehta, V. (2025). Analyzing compressive, flexural, and tensile strength of concrete incorporating used foundry sand: Experimental and machine learning insights. Archives of Computational Methods in Engineering.

Mehta, V., Thakur, M. S., & Chey, M. H. (2025). Enhancing seismic design accuracy of RC beam-column joints: Modifier-based approach for shear strength predictions. Structures.

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Adaptive simulation and data-driven hybrid modeling for predicting shear strength and failure modes of interior reinforced concrete beam-column joints. Structures.

Khurshid Hussain | Artificial Intelligence in Statistics | Research Excellence Award

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Mr. Khurshid Hussain | Artificial Intelligence in Statistics | Research Excellence Award

Kiost | South Korea

Mr. Khurshid Hussain is a dynamic researcher whose work spans advanced automotive engineering, semiconductor design, integrated sensing and communications, and AI-driven signal processing, establishing him as a multidisciplinary contributor across next-generation wireless, cybersecurity, and intelligent vehicular systems. He holds a Master’s degree in Advanced Automotive Engineering from Sun Moon University, South Korea, where he specialized in high-performance millimeter-wave circuit design with emphasis on 60 GHz digital variable-gain amplifiers, beamforming architectures, low-power attenuators, and chip-level ISAC systems for secure and intelligent communication. His research extends into geomatics and remote sensing, focusing on multimodal mapping using optical, SAR, and LiDAR streams, change detection, 3D reconstruction, and uncertainty-aware geospatial pipelines, alongside self-supervised and weak-supervised learning approaches for large-scale spatial data modeling. He is the inventor of a patented transistor-array-based variable attenuator and has authored an expanding collection of peer-reviewed publications in leading journals such as Electronics, IEEE Access, Applied Sciences, and IEEE Transactions, addressing topics ranging from radar–communication co-design and ultrasonic 3D beamforming sensors to predictive maintenance of aerospace components, OTFS-based V2X ISAC architectures, and AI-enhanced signal intelligence. His scholarly profile includes 9 documents, 77 citations, and an h-index of 4, reflecting his growing influence in mmWave IC design, wireless sensing, and AI-integrated communication. Khurshid has delivered technical presentations at major international conferences covering maritime IT convergence, high-frequency amplifier design, battery analytics, advanced beamforming, and power-efficient RF front-end systems. His expertise spans Cadence, HFSS, Python, MATLAB, OrCAD, cybersecurity tools, and vector network analyzers, reinforced by experience in transceiver integration, AI-chip convergence, intrusion detection systems, battery research, and embedded engineering. Earlier, he completed his B.Sc. in Electrical Engineering with a focus on IoT-based renewable-energy automation, where he developed sensor-driven, cloud-connected, and energy-efficient systems. Fluent in English and active in multicultural environments, Khurshid is known for his creativity, leadership, communication skills, and passion for innovation, continually advancing secure, intelligent, and energy-efficient technologies for the automotive, wireless, and sensing industries.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Hussain, K., & Yoo, J. (2025). Low-latency marine-based OTFS echo parameter estimation enabled by AI. Sensors, 25(23), Article 7104. DOI: 10.3390/s25237104

Hussain, K., Ali, E. M., Hussain, W., Raza, A., & Elkamchouchi, D. H. (2025). Robust OTFS-ISAC for vehicular-to-base station end-to-end sensing and communication. Electronics, 14(21), Article 4340. DOI: 10.3390/electronics14214340

Hussain, K., Jeon, W., Lee, Y., Song, I., & Oh, I. (2025). CMOS-compatible ultrasonic 3D beamforming sensor system for automotive applications. Applied Sciences, 15(16), Article 9201. DOI: 10.3390/app15169201

Hussain, K., & Oh, I. (2024). Joint radar, communication, and integration of beamforming technology. Electronics, 13(8), Article 1531. DOI: 10.3390/electronics13081531

Hussain, K., & Oh, I. (2024). Review of joint radar, communication, and integration of beam-forming technology. Preprint. DOI: 10.20944/preprints202404.0208.v1

Kostiantyn Kotenko | Operations Research and Statistical Optimization | Best Researcher Award

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Prof. Kostiantyn Kotenko | Operations Research and Statistical Optimization | Best Researcher Award

S.P.Timoshenko Institute of Mechanics | Ukraine

Dr. Kostyantin Kotenko is an Professor in the Department of Theoretical Mechanics at the Kyiv National University of Construction and Architecture (KNUCA), Ukraine, specialising in building structures and civil engineering systems. He holds the degree of Candidate of Technical Sciences (equivalent to PhD) and has developed extensive expertise in the dynamics of layered, or sandwich, shell structures with inhomogeneous fillers. A graduate of KNUCA, Dr. Kotenko’s academic background is rooted in the theory and design of complex structural systems, and his research focuses on the dynamic response and stability of multi-layered shells subjected to transient, impact, and nonstationary loads. Over his career, he has co-authored numerous influential papers in international journals, exploring dynamic responses of domes, cylindrical and conical shells with inhomogeneous elastic cores. His work has earned recognition for its analytical depth and contribution to advancing the field of structural dynamics. According to his Scopus profile, Dr. Kotenko has authored 10 scientific publications, received approximately 16 citations, and holds an h-index of 1, reflecting his active engagement and growing impact in the global research community. At KNUCA, he teaches theoretical mechanics and structural dynamics, supervises postgraduate research, and contributes to academic development through innovative research on layered shell mechanics. His continuing investigations into the stress–strain behaviour and stability of multi-layered systems have practical applications in modern civil and aerospace engineering. Dr. Kotenko’s scholarly contributions, combined with his dedication to education and applied mechanics, establish him as a prominent specialist in the field of dynamic analysis of layered and composite structural shells.

Featured Publications

Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

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Dr. Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

University of Lisbon | Portugal

Dr. Rasool Taban, Ph.D, is a distinguished Data Scientist currently affiliated with Technical University Institute – University of Lisbon, where he continues to advance the frontiers of Artificial Intelligence and Data Science. His academic journey began in Computer Engineering and evolved into a profound focus on Artificial Intelligence during his M.Sc. studies at the University of Tehran, where he graduated with honors in Artificial Intelligence and Robotics. His early research centered on developing an automated screening system designed to assist in diagnosing Autism Spectrum Disorder in children, demonstrating his ability to merge technology with meaningful social impact. Dr. Taban recently earned his Industrial Ph.D. at Institute – University of Lisbon, funded by the prestigious Marie Curie BIGMATH project, where his research specialized in addressing one of the most persistent challenges in statistical learning-imbalanced data. He successfully developed three novel balancing techniques, each tailored to optimize performance across different variable classes, making significant contributions to data reliability and analytical accuracy in machine learning models. With two published journal papers indexed in Scopus and SCI, Dr. Taban’s scholarly work reflects both academic rigor and applied innovation. He has also participated in multiple research and industry projects, collaborating with institutions such as the SDG Group, CIF/N26, Evenco International, and CTAD–Tehran Autism Center. His involvement as part of the editorial team for the International Conference on Robotics and Mechatronics (ICRoM) further underscores his leadership in advancing interdisciplinary research. Dr. Taban’s primary research interests include imbalanced data, statistical learning, data science, and financial data modeling. His contributions have not only expanded methodological knowledge in statistical computing but have also bridged the gap between theoretical frameworks and real-world data-driven applications, reflecting his commitment to excellence in both academia and industry.

Profiles:  Google Scholar | Linked In

Featured Publications

Taban, R., Nunes, C., & Oliveira, M. R. (2023). RM-SMOTE: A new robust balancing technique.

Taban, R., Nunes, C., & Oliveira, M. R. (2025). Mixed-robROSE: A novel balancing technique tailored for mixed-type datasets.

Bozorgnia, F., Arakelyan, A., & Taban, R. (2023). Graph-based semi-supervised learning for classification of imbalanced data. Submitted to Conference ENUMATH.

Shahri, M. A., & Taban, R. (2021). ML revolution in NLP: A review of machine learning techniques in natural language processing. Journal of Applied Intelligent Systems & Information Sciences (JAISIS), 2(1), 2.

Taban, R., Parsa, A., & Moradi, H. Tip-toe walking detection using CPG parameters from skeleton data gathered by Kinect. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 9).

Tao Zhong | Machine Learning and Statistics | Best Researcher Award

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Dr. Tao Zhong | Machine Learning and Statistics | Best Researcher Award

Sun Yat-sen University | China

Dr. Zhong Tao is a dedicated interdisciplinary researcher specializing in environmental engineering, material science, and computational modelling. A native of Chongqing, China, he is a member of the Communist Party of China and currently based in Guangzhou. He earned his Bachelor’s degree in Environmental and Ecological Engineering with a minor in Computer Science and Technology from Sichuan Agricultural University, followed by a Master’s in Environmental Science and Engineering from Guangxi University under Prof. Yu Zebin, and is pursuing his Doctor of Engineering (Ph.D.) in Resources and Environment at Sun Yat-sen University under Prof. He Chun. His research focuses on the design and development of high-activity environmental functional materials for atmospheric and water pollutant removal, catalytic ozonation, and clean-energy catalysis, including hydrogen production via water splitting. He also employs Density Functional Theory (DFT) to analyze catalytic materials and pollutant molecular structures, building structure–property relationships to guide experiments. Dr. Zhong has contributed to 31 SCI-indexed papers, including 11 as first or co-first author, and applied for 5 patents, with 4 granted. His ongoing research includes national and provincial projects as principal investigator or key contributor. He has received multiple national and university-level scholarships and awards for academic excellence, innovation, and leadership. His Scopus metrics reflect a growing international influence, with an h-index of 10, 22 documents, and over 343 citations, underscoring his strong academic productivity. Known for his rigorous research approach, interdisciplinary collaboration, and mentoring of peers and students, Dr. Zhong also pursues interests in history, literature, and sports, maintaining an optimistic, resilient, and disciplined outlook that complements his scientific career.

Profiles: Scopus 

Featured Publications

Guo, X., Yao, Z., Long, X., Zeng, L., Wang, C., Fang, Z., Zhong, T., Tian, S., Shu, D., & He, C. (2025). Recent advances in tailored nanostructured ozonation catalysts for enhanced VOCs removal: Synergistic optimization of scale configuration and electronic microenvironment.

Zhong, T., Yao, Z., Zeng, L., Zhao, H., Long, X., Li, T., Tian, S., & He, C. (2025). Manipulating spin-configuration via electron reverse overflow to dynamically tune the adsorption behavior of sulfur-containing intermediates for enhanced sulfur resistance.

Tushar Bhoite | Bayesian Networks and Decision Theory | Best Researcher Award

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Dr. Tushar Bhoite | Bayesian Networks and Decision Theory | Best Researcher Award

Dr. Tushar Bhoite | MES’s Wadia College of Engineering | India

Dr. Tushar Devidas Bhoite, Ph.D. in Mechanical Technology, M.E. in Machine Design, with over sixteen years of professional experience (including twelve in the automobile industry and four in academics), is a leading researcher and practitioner in integrating advanced IT technologies in auto-component manufacturing. His research interests encompass Industry 4.0, Industrial Internet of Things (IIoT), Digital Twin systems, Generative AI, predictive maintenance, neural and Bayesian networks, real-time monitoring, optimization and efficiency improvements in production environments. To date, he has authored 6 international journal papers and 3 international conference papers, co-authored 2 textbooks, and filed 2 patents, contributing to both theoretical and applied advancements. His master’s project, awarded from RGSTC Mumbai, stands as a landmark in his research portfolio, he has an h-index of 1 and over 1 citations as per his Scopus profile, reflecting strong scholarly impact. In his industrial roles, Dr. Bhoite has successfully led IOT implementations in assembly and press shops, improved Overall Equipment Effectiveness, standardized work systems, deployed TPM and Kaizen methodologies, and developed innovative solutions such as a wet-waste treatment machine. He currently serves as Assistant Professor in Automation & Robotics Engineering at MES’s Wadia College of Engineering, Pune, and continues consulting in production efficiency, resource optimization, and technology integration in manufacturing enterprises.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Bhoite, T. D., Pawar, P. M., & Gaikwad, B. D. FEA based study of effect of radial variation of outer link in a typical roller chain link assembly. International Journal of Mechanical and Industrial Engineering, 1, 65–70.

Bhoite, T. D., & Buktar, R. B. (2025). Productivity enhancement in Indian auto component manufacturing supply chain with IoT using neural networks. Production, 35, e20240047.

Bhoite, T. D., Buktar, R. B., Mahalle, P. N., Khond, M. P., Pise, G. S., & More, Y. Y. (2025). Productivity enhancement in the Indian auto component manufacturing supply chain through IoT, digital twins with generative AI, and stacked encoder-enhanced neural networks. Operations Research Forum, 6(4), 143.

Bhoite, D. T., & Buktar, R. (2025). An investigation into revolutionizing auto component manufacturing: An IoT-based approach for improved productivity and waste elimination. Journal of Information Systems Engineering and Management, 10(9s), 409–429.

Bhoite, D. T., Buktar, R., & Kannukkadan, G. (2024). Leveraging IoT in auto component manufacturing to monitor surface roughness and tool temperature. Journal of Electrical Systems, 20(2s), 563–574.*

Martin Ferrand| Statistical Modeling and Simulation | Best Researcher Award

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Dr. Martin Ferrand | Statistical Modeling and Simulation | Best Researcher Award

EDF R&D | France

Dr. Martin Ferrand is an accomplished researcher and engineer whose academic and professional journey has been defined by excellence in fluid dynamics, smoothed particle hydrodynamics (SPH), and computational modelling, and he is currently affiliated with the University of Manchester in the MACE Department, where he has undertaken advanced research as part of his MPhil studies, focusing on SPH with the specialized software SPARTACUS developed at EDF R&D; his academic foundations were laid at École des Ponts ParisTech, one of France’s most prestigious engineering institutions, where he specialized in mechanics, fluid mechanics, and data processing, complemented by intensive preparatory studies in advanced mathematics and physics at Lycée du Parc, Lyon, following his French Scientific Baccalaureate with distinction in 2004, which marked the start of a career shaped by intellectual rigor and scientific curiosity. Professionally, Ferrand has gained diverse and impactful experience, including at EDF R&D in Chatou, France, where he developed turbulence models incorporating buoyancy effects and enhanced the hydrodynamic reproduction of the Berre lagoon using TELEMAC3D, and at Imperial College London, where he expanded his expertise into stochastic processes by designing models to reproduce rainfall patterns for optimizing sewage infrastructure, while his immersion at Bouygues Construction gave him early exposure to industry practices, broadening his adaptability. His academic impact is evident through a strong research record comprising 70 scholarly documents, collectively cited 749 times, with an h-index of 11, reflecting the recognition, influence, and sustained relevance of his work in computational and environmental fluid mechanics; his expertise extends across theoretical, computational, and applied domains, establishing him as a versatile contributor to advancing both scientific understanding and engineering practice. In addition to his technical accomplishments, Ferrand is multilingual, fluent in French and English with working knowledge of German, enabling him to collaborate effectively in international research environments, while his interests outside academia, including football, cooking, and model train construction, showcase his creativity, discipline, and appreciation for teamwork, all of which complement his professional excellence. Overall, Martin Ferrand stands as a dedicated scholar and engineer whose combination of intellectual achievement, technical expertise, and international experience continues to make a significant contribution to his field and positions him as a rising figure in the global scientific community.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

“Unified semi‐analytical wall boundary conditions for inviscid, laminar or turbulent flows in the meshless SPH method”

“Unified semi-analytical wall boundary conditions applied to 2-D incompressible SPH”

“An innovative method based on CFD to simulate the influence of photovoltaic panels on the microclimate in agrivoltaic conditions”

“A time-step-robust algorithm to compute particle trajectories in 3-D unstructured meshes for Lagrangian stochastic methods”

“Unsteady open boundaries for SPH using semi-analytical conditions and Riemann solver in 2D”

Changqing Cao | High-Dimensional Data Analysis | Best Researcher Award

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Prof. Dr. Changqing Cao | High-Dimensional Data Analysis | Best Researcher Award

Prof. Dr. Changqing Cao | Xidian University | China

Dr. Cao Changqing is a distinguished researcher in the field of optoelectronic technology, remote sensing, and artificial intelligence–based image processing. With a career rooted in innovation and discovery, he has consistently contributed to advancing the frontiers of photonics and optical engineering. His dedication to academic excellence is reflected in his extensive involvement in high-impact publications, editorial roles, and global scientific recognition. A lifelong learner and mentor, Dr. Cao has guided numerous projects that bridge theoretical frameworks with practical applications, creating lasting impact across industries and research communities. His recognition as one of the world’s top scientists underscores the breadth of his expertise and the significance of his research contributions. Through his work, Dr. Cao has earned a reputation not only as a skilled academic but also as a visionary scientist committed to developing cutting-edge technologies that benefit society and foster interdisciplinary collaboration.

Profiles

Orcid
Scopus

Education

Dr. Cao pursued his higher education at Xidian University, where he dedicated nearly a decade to mastering the intricacies of optical engineering. Beginning with a foundation in undergraduate studies, he advanced seamlessly into postgraduate research, ultimately earning his doctorate in the same field. His academic journey was marked by an immersion in the principles of photonics, laser systems, and advanced optical imaging, disciplines that later became central to his professional expertise. The rigorous training he received equipped him with both theoretical knowledge and experimental skills, enabling him to explore challenging problems in optoelectronics. His progression from bachelor’s to doctoral studies at the same institution reflects a continuous commitment to deep specialization while maintaining a broad perspective on technological applications. This academic background provided the cornerstone for his innovative research career, nurturing the analytical rigor and creativity that define his scholarly contributions to modern optical and remote sensing technologies.

Experience

Dr. Cao began his professional career at Xidian University, where he continues to serve as a faculty member specializing in optoelectronics engineering. Over the years, he has developed a strong academic and professional identity by combining teaching, research, and scientific leadership. His responsibilities span supervising advanced research projects, mentoring young scholars, and contributing to international collaborations. Dr. Cao’s experience has also extended into peer-review and editorial activities for leading scientific journals such as those under the OSA Optica Publishing Group, IEEE, MDPI, and Wiley. Serving as both a reviewer and an editor has positioned him at the forefront of evaluating and shaping scientific advancements in his field. His work is characterized by a blend of experimental exploration and applied engineering, ensuring that his research remains both academically rigorous and technologically relevant. This long-standing experience illustrates his dedication to scientific excellence and knowledge dissemination worldwide.

Research Interests

Dr. Cao’s research interests lie at the intersection of optoelectronic technology, remote sensing, and artificial intelligence–driven image analysis. His work often bridges fundamental optical theories with advanced engineering practices, producing solutions that enhance imaging quality, detection accuracy, and data interpretation in complex environments. He has explored areas such as optical heterodyne detection, interferometric imaging, and laser dynamics, with applications spanning satellite imaging, photonics integration, and high-speed optical systems. A key theme of his research is the application of machine learning and clustering algorithms to improve image processing and modulation format identification, which has direct relevance in communication and sensing technologies. He has also contributed significantly to the study of light scattering, compressed sensing in remote imaging, and phase compensation algorithms. This combination of expertise highlights his versatility in applying optics and AI to solve real-world challenges, reflecting both innovative thinking and a strong commitment to interdisciplinary advancement.

Awards & Recognitions

Dr. Cao’s career is his recognition in the prestigious Top Two Percent Global Scientists List, which highlights his global influence and outstanding contributions to optical engineering. This honor underscores the impact of his research and the high regard in which he is held within the international scientific community. Beyond this recognition, Dr. Cao’s roles as an editor and reviewer for top-tier journals further attest to his academic reputation and professional achievements. These positions not only reflect his expertise but also demonstrate his responsibility in guiding the quality of global scientific literature. His award recognition is a testament to his years of dedication, continuous innovation, and ability to address complex problems in optoelectronics. Such honors contribute to cementing his position as a thought leader whose work inspires fellow researchers and fosters the next generation of advancements in photonics and remote sensing.

Publication Top Notes

An Improved Satellite ISAL Imaging Vibration Phase Compensation Algorithm Based on Prior Information and Adaptive Windowing

Journal: Remote Sensing (2025)
Authors: Chenxuan Duan, Hongyuan Liu, Xiaona Wu, Jian Tang, Zhejun Feng, Changqing Cao

Calibration of 16 × 16 SOI optical phased arrays via improved SPGD algorithm

Journal: Optics and Laser Technology (2023)
Authors: Z. Wang, B. Wu, J. Liao, X. Li, C. Wang, Y. Sun, L. Jin, J. Feng,  Changqing Cao

Factors influencing the performance of optical heterodyne detection system

Journal: Optics and Lasers in Engineering (2023)
Authors: Z. Wu, C. Cao, Z. Feng, S. Ye, M. Li, B. Song, R. Wei

Improving distance imaging accuracy through temporal position correction with phase difference compensation

Journal: Applied Optics (2023)
Authors: Z. Wu, C. Cao, Z. Feng, X. Wu, C. Duan, H. Liu

Innovative OPA-based optical chip for enhanced digital holography

Journal: Optics Express (2023)
Authors: Z. Wang, L.I.U. Linke, P. Jiang, J. Liao, X.U. Jiamu, S.U.N. Yanlnig, J.I.N. Li, L.U. Zhenzhong, J. Feng, C. Cao

Conclusion

Dr. Cao Changqing embodies the qualities of a dedicated researcher, visionary innovator, and respected academic leader. His journey through higher education, research, and professional service demonstrates a lifelong commitment to pushing the boundaries of knowledge in optoelectronics and photonics. With an impressive body of published work, editorial engagements, and global recognition, he has established himself as a prominent figure in the scientific community. His achievements illustrate not only personal excellence but also a broader contribution to advancing the capabilities of imaging and optical systems for diverse applications. As an educator, he inspires students and collaborators, fostering an environment of curiosity and innovation. As a scientist, he delivers groundbreaking work that continues to shape the field. With such a strong record of achievements, Dr. Cao represents the ideal candidate for prestigious recognition, serving as a role model for future generations of researchers and a driving force in global technological progress.