Prof. Dr. Beatriz Defez | Sensors | Research Excellence Award

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Prof. Dr. Beatriz Defez | Sensors | Research Excellence Award

Professor | Valencia Polytechnic University | Spain

Beatriz Defez García is an accomplished scholar whose work bridges engineering graphics, materials science, acoustic behavior, assistive technologies, and advanced computational modeling. Her research notably spans finite-element analysis of ceramic materials, acoustic characterization of engineered structures, fluid–solid interactions, and the development of innovative navigation systems to support individuals with visual impairments. She has made influential contributions to the fields of human–machine interaction, sensory guidance technologies, telemedicine, and computer-assisted image segmentation, particularly in dermatological applications. Her scholarship encompasses collaborative, interdisciplinary projects addressing societal and industrial needs through engineering innovation. With a strong record of productivity, she has authored 30 Scopus-indexed documents and accumulated 188 citations in Scopus, reflecting an h-index of 7, while her Google Scholar profile reports 486 citations, an h-index of 11, and an i10-index of 11, demonstrating the sustained visibility and relevance of her research. Her most widely cited studies include pioneering prototypes for real-time assistive navigation, analytical and experimental investigations of bubble and drop oscillations, and advanced techniques for usability evaluation in digital learning environments. Across her publications, Defez García integrates computational methods, experimental validation, and user-centered design principles to advance engineering solutions that improve material performance, enhance accessibility, and support health-related digital transformation. Her recent work also highlights growing contributions in telemedicine, sustainable digitalization, and biomedical image processing, reinforcing her role as a multidisciplinary researcher shaping both technological innovation and applied scientific practice.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Dunai, L., Fajarnes, G. P., Praderas, V. S., Defez Garcia, B., & Lengua, I. (2010). Real-time assistance prototype—A new navigation aid for blind people. IECON Annual Conference on IEEE Industrial Electronics Society, 1–6.

Milne, A. J. B., Defez, B., Cabrerizo-Vílchez, M., & Amirfazli, A. (2014). Understanding (sessile/constrained) bubble and drop oscillations. Advances in Colloid and Interface Science, 203, 22–36.

Dunai, L., Peris-Fajarnés, G., Lluna, E., & Defez, B. (2013). Sensory navigation device for blind people. The Journal of Navigation, 66(3), 349–362.

Fraile-Garcia, E., Ferreiro-Cabello, J., Defez, B., & Peris-Fajanes, G. (2016). Acoustic behavior of hollow blocks and bricks made of concrete doped with waste-tire rubber. Materials, 9(12), 962.

Maria, M. S., Silvia, A. N., Beatriz, D. G., Andrew, D., & Guillermo, P. F. (2022). Health care in rural areas: Proposal of a new telemedicine program assisted from the reference health centers, for sustainable digitization and its contribution to carbon reduction. Heliyon, 8(7).

Mr. Ikram Ullah | Biological Sciences | Research Excellence Award

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Mr. Ikram Ullah | Biological Sciences | Research Excellence Award

Northwest A&F University | China

Mr. Ikram Ullah is an emerging life science researcher specializing in plant genomics, molecular biology, and stress physiology, with a particular focus on horticultural crops. His research centers on genome-wide identification, characterization, and functional analysis of key gene and transcription factor families that regulate plant growth, defense pathways, and stress responses. He has contributed significantly to understanding bHLH, CRK, YABBY, callose enzyme, and Tubby-Like Protein gene families, providing new insights into their structural diversity, expression dynamics, and regulatory roles under biotic and abiotic stresses. His work extends to plant–pathogen interactions, root microbiota, cold stress management, heat stress mitigation, and hormonal modulation in economically important crops such as rose, cucumber, wheat, pepper, Brassica species, and tuberose. He is skilled in advanced molecular techniques, gene expression profiling, microbial metagenomics, and bioinformatics tools that support integrative genomic analyses. His research contributions are reflected in his citation metrics, with notable visibility across global indexing platforms. According to Scopus, his work has received around 1,900 citations from approximately 1,606 documents with an h-index of 23. His broader academic footprint on Google Scholar further highlights his growing impact within plant sciences and biotechnology. Mr. Ullah’s research continues to advance molecular breeding, stress-resilience strategies, and genomic understanding essential for sustainable horticulture and crop improvement.

Publication Profile

Scopus | ORCID

Featured Publications

Ullah, I., Yuan, W., Uzair, M., Li, S., Rehman, O., Nanda, S., & Wu, H. (2022). Genome-wide identification and expression analysis of the RcYABBYs reveals their potential functions in rose under Botrytis cinerea infection. Horticulturae, 8, 989.

Ullah, I., Ponsalven, A., Abbas, A., Hussain, S., & Nanda, S. (2022). Molecular characterization of bHLH transcription factor family in rose under Botrytis cinerea infection.

Ullah, I., et al. (2025). Molecular mechanisms and genomic strategies for enhancing stress resilience in pepper crop. Scientia Horticulturae, 352, 114403.

Tang, Y., Wu, J., Zhao, M., Guo, Y., Ullah, I., & Wu, H. (2020). Complete genome sequence of begonia flower breaking virus, a novel member of the genus Potyvirus. Archives of Virology, 165, 1915–1918.

Nanda, S., Rout, P., Ullah, I., Swapna, R., Velagala, V. R., Ritesh, K., & Wu, H. (2023). Genome-wide identification and molecular characterization of CRK gene family in cucumber under cold stress and sclerotium rolfsii infection. BMC Genomics, 24, 219.

Dr. Shuihuan Guo | Agriculture Technology | Research Excellence Award

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Dr. Shuihuan Guo | Agriculture Technology | Research Excellence Award

Henan Agricultural University | China

Dr. Shuihuan Guo is a food science researcher specializing in fruit and vegetable storage, postharvest physiology, and the molecular biology of horticultural crops, with a strong focus on grapes. His work explores the mechanisms underlying stress responses, anthocyanin biosynthesis, quality formation, and the regulation of plant metabolic pathways that influence fruit development and postharvest quality. He has contributed significantly to understanding how environmental cues, hormonal signaling pathways, and gene regulatory networks shape the biochemical and physiological traits of grape berries under both natural and controlled conditions. His research particularly emphasizes drought stress, abscisic acid (ABA) signaling, melatonin biosynthesis, and microRNA-mediated regulatory mechanisms related to berry coloration, metabolite accumulation, and stress tolerance in different grape genotypes. Through integrating advanced molecular techniques with applied cultivation strategies, he aims to enhance fruit quality, improve resilience to abiotic stress, and support sustainable viticulture practices. His scientific contributions are recognized through measurable academic impact, including Scopus metrics of 219 citations, 15 documents, and an h-index of 8, supported by growing citation activity across Google Scholar as well. His publications appear in high-impact journals in horticulture, food chemistry, and plant molecular biology, reflecting his collaborative work and his commitment to advancing postharvest and viticultural science. Overall, his research bridges fundamental molecular insights with practical applications to support high-quality production and efficient cultivation of grapes and other horticultural crops.

Publication Profile

Scopus

Featured Publications 

Guo, S., Zhang, M., Feng, M., et al. (2024). miR156b-targeted VvSBP8/13 functions downstream of the ABA signal to regulate anthocyanin biosynthesis in grapevine fruit under drought. Horticulture Research, 11, 293.

Guo, S., Xu, T., Shi, T., et al. (2020). Cluster bagging promotes melatonin biosynthesis in the berry skins of Vitis vinifera cv. Cabernet Sauvignon and Carignan during development and ripening. Food Chemistry, 305, 125502.

Guo, S., Xu, T., Ju, Y., et al. (2023). MicroRNAs behave differently to drought stress in drought-tolerant and drought-sensitive grape genotypes. Environmental and Experimental Botany, 207, 105233–105248.

Guo, S., Yang, B., Wang, X., et al. (2021). ABA signaling plays a key role in regulated deficit irrigation-driven anthocyanins accumulation in ‘Cabernet Sauvignon’ grape berries. Environmental and Experimental Botany, 181, 104290.

Dr. Yan Gao | Mems Sensors | Research Excellence Award

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Dr. Yan Gao | Mems Sensors | Research Excellence Award

Professor | Sun Yat-sen University | China

Dr. Yan Gao is a distinguished researcher in geotechnical engineering whose work advances fundamental understanding of the micromechanical behavior of geomaterials, particularly aging, creep, particle breakage, and structuration effects in sand. His research integrates advanced numerical simulations using the Discrete Element Method with innovative laboratory characterization techniques, including wave-based measurements, tactile pressure sensors, and MEMS-enabled Smart Soil Particles (SSP). Through these approaches, he uncovers the micro–macro mechanisms governing deformation, stiffness evolution, and kinematic responses in granular soils under various loading conditions. His investigations also extend to the development and application of GeoMEMS technologies for monitoring geologic hazards and improving early warning systems for urban rail transit. Additionally, he contributes to sustainable geotechnical engineering through research on novel and eco-friendly building materials. Dr. Gao’s scientific contributions have resulted in significant academic visibility, reflected in 158 Scopus citations, 144 citing documents, 42 publications, and an h-index of 8 on Scopus, with complementary recognition on Google Scholar. His work provides crucial insights for infrastructure safety, coastal and marine geotechnics, foundation engineering in calcareous sands, and next-generation smart sensing technologies in geomechanics.

Publication Profile

Scopus

Featured Publications

Gao, Y., Shi, T. G., Yuan, Q., Shi, X., & Sun, K. T. (2024). Particle gradation effects on creep characteristics and the underlying mechanism in calcareous sand. Construction and Building Materials, 424, 135952.

Gao, Y., Shi, T. G., Yuan, Q., & Sun, K. T. (2024). The creep characteristics and related evolution of particle morphology for calcareous sand. Powder Technology, 431, 119077.

Gao, Y., Shi, X., Yuan, Q., Sun, L., & Sun, K. T. (2024). Particle breakage and uneven settlement characteristics of calcareous sand foundation. Journal of Building Engineering, 100, 111662.

Gao, Y., Chen, Q., Yuan, Q., & Wang, Y. H. (2023). The kinematics and micro mechanism of creep in sand based on DEM simulations. Computers and Geotechnics, 153, 105082.

Gao, Y., & Wang, Y. H. (2016). Experimental characterization of deformation, stiffness, and contact force distributions of sand during secondary compression and rebound. Canadian Geotechnical Journal, 53(5), 889–898.

Prof. Dr. Gary Wong | Computer Science Education | Research Excellence Award

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Prof. Dr. Gary Wong | Computer Science Education | Research Excellence Award

Faculty of Education | The University of Hong Kong | Hong Kong

Prof. Dr. Gary Ka Wai Wong is a leading scholar in computational thinking, computer science education, and artificial intelligence (AI)–enhanced learning, widely recognized for his interdisciplinary contributions to digital literacy, immersive learning environments, and K–12 STEM innovation. His research explores how constructionist pedagogies, data-driven precision learning, and extended reality experiences foster cognitive development, problem-solving, and twenty-first century competencies in learners. He has played a pivotal role in shaping international frameworks for digital literacy and AI education, including large-scale studies that examine teachers’ technological adoption, students’ computational thinking development, and school-level implementation of coding and STEM initiatives. His body of work reflects strong methodological diversity, spanning design-based research, meta-analysis, psychometric assessment, and mixed-methods evaluations of learning technologies. His scholarly influence is demonstrated by substantial global citations: Scopus records over 1,218 citations across 1,071 citing documents with an h-index of 18, while Google Scholar reports more than 3,111 citations with an h-index of 24 and 48 i10-index publications. His contributions extend to immersive virtual reality in science learning, digital competence assessment, and early-age unplugged and plugged computational thinking activities. His research continues to inform international policy, curriculum innovations, and empirical understandings of how AI, coding, and digital tools can be meaningfully integrated into education to cultivate future-ready learners.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Wong, G. K. W., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K–12: Where to start? ACM Inroads, 11(1), 20–29.

Saxena, A., Lo, C. K., Hew, K. F., & Wong, G. K. W. (2020). Designing unplugged and plugged activities to cultivate computational thinking: An exploratory study in early childhood education. The Asia-Pacific Education Researcher, 29(1), 55–66.

Wong, G. K. W., & Cheung, H. Y. (2020). Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. Interactive Learning Environments, 28(4), 438–450.

Law, N. W. Y., Woo, D. J., De la Torre, J., & Wong, K. W. G. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2. UNESCO Institute for Statistics.

Lui, A. L. C., Not, C., & Wong, G. K. W. (2023). Theory-based learning design with immersive virtual reality in science education: A systematic review. Journal of Science Education and Technology, 32(3), 390–432.

Mr. Xingwang Bian | Traveling Wave Tube | Research Excellence Award

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Mr. Xingwang Bian | Traveling Wave Tube | Research Excellence Award

Senior Engineer | Beijing Vacuum Electronics Research Institute | China

Mr. Xingwang Bian is a China-based researcher whose work significantly advances the field of vacuum electronics, high-frequency device engineering, and terahertz (THz) communication technologies. His research focuses on the design, optimization, and experimental characterization of G-band traveling wave tubes (TWTs), slow-wave structures, and high-power millimeter-wave sources that support next-generation radar, wireless communication, and sensing systems. Bian has contributed to innovations in modified folded waveguide configurations, wide-band continuous-wave amplifiers, pulsed-wave THz devices, and hybrid photonic–electronic architectures that enable communication beyond 300 GHz. His publications demonstrate strong expertise in device modeling, electromagnetic design, vacuum electronic component fabrication, and system-level performance enhancement. With 17 research documents indexed and 155 citations in Scopus, along with citations registered across 118 scholarly works, Bian has established a growing academic footprint. His h-index in Scopus is 4, reflecting impactful early-career contributions in a highly specialized domain. His citation metrics in Google Scholar also highlight the visibility and technical relevance of his work within the global vacuum electronics and THz technology communities. Through collaborative projects, experimental demonstrations, and high-frequency device innovations, Bian’s research continues to support emerging breakthroughs in ultra-high-speed wireless communication and advanced radar systems. His contributions underscore the importance of compact, efficient, and high-power THz sources that can shape the future landscape of communication engineering and microwave electronics.

Publication Profile

Scopus | ORCID

Featured Publications 

Bian, X., Pan, P., Du, X., Feng, Y., Li, Y., Song, B., & Feng, J. (2025). Design and experiment of modified folded waveguide slow wave structure for 60-W G-band traveling wave tube. IEEE Microwave and Wireless Technology Letters.

Bian, X., Pan, P., Xian, S., Yang, D., Zhang, L., Cai, J., & Feng, J. (2025). A G-band pulsed wave traveling wave tube for THz radar. Preprints.

Zhu, M., Cai, Y., Zhang, L., Zhang, J., Hua, B., Ma, K., Ding, J., Bian, X., et al. (2025). Surpassing kilometer-scale terahertz wireless communication beyond 300 GHz enabled by hybrid photonic–electronic synergy. Research Square.

Bian, X., Pan, P., Du, X., Song, B., Zhang, L., Cai, J., & Feng, J. (2024). Demonstration of a high-efficiency and wide-band 30-W G-band continuous wave traveling wave tube. IEEE Electron Device Letters.

Feng, Y., Bian, X., Song, B., Li, Y., Pan, P., & Feng, J. (2022). A G-band broadband continuous wave traveling wave tube for wireless communications. Micromachines.

Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

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Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

Professor | Lunghwa University of Science and Technology | Taiwan

Mr. Ro-Yu Wu is a Taiwan-based researcher recognized for his contributions to combinatorial algorithms, graph theory, and efficient data structures, particularly within the domains of ranking and unranking methods, Hamiltonian graph properties, and algorithmic generation of combinatorial objects. As a productive scholar in theoretical computer science and industrial management, his work emphasizes the design of loopless, lexicographic, and Gray code–based algorithms that enhance computational efficiency and fault-tolerant communication in complex networks. His research is well acknowledged in the global academic community, as reflected in his Scopus record with 45 documents, 308 citations across 189 citing works, and an h-index of 10. On ResearchGate, he maintains an active profile with 47 publications, over 6,300 reads, and 367 citations. These metrics highlight his growing influence, especially in areas involving structured graph traversal, spanning-tree generation, and the analytical foundations supporting optimization and data broadcasting systems. His work frequently explores practical algorithmic strategies for high-performance computing environments, providing innovative insights for fault-tolerant network design and combinatorial enumeration. Mr. Wu’s collaborations span multi-author research teams, contributing to advancements published in high-impact venues such as Theoretical Computer Science, The Journal of Supercomputing, Journal of Combinatorial Optimization, and Optimization Letters. His ongoing research continues to shape efficient computational paradigms for combinatorial structures, making him a relevant contributor to the future of theoretical and applied algorithmic studies.

Profile

Scopus

Featured Publications 

Xie, Z., Wu, R.-Y., & Shi, L. (2025). Ranking and unranking algorithms for derangements based on lexicographical order. Theoretical Computer Science.

Pai, K.-J., Wu, R.-Y., Peng, S. L., & Chang, J. M. (2023). Three edge-disjoint Hamiltonian cycles in crossed cubes with applications to fault-tolerant data broadcasting. The Journal of Supercomputing.

Chang, Y. H., Wu, R.-Y., Chang, R. S., & Chang, J. M. (2022). Improved algorithms for ranking and unranking (k, m)-ary trees in B-order. Journal of Combinatorial Optimization.

Wu, R.-Y., Tseng, C. C., Hung, L. J., & Chang, J. M. (2022). Generating spanning-tree sequences of a fan graph in lexicographic order and ranking/unranking algorithms. International Symposium on Combinatorial Optimization.

Chang, Y. H., Wu, R.-Y., Lin, C. K., & Chang, J. M. (2021). A loopless algorithm for generating (k, m)-ary trees in Gray code order. Optimization Letters.

Prof. Changfang Chen | Medical Image Processing | Research Excellence Award

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Prof. Changfang Chen | Medical Image Processing | Research Excellence Award

Associate Professor | Qilu University of Technology | China

Prof. Changfang Chen is an associate professor at the Shandong Institute of Artificial Intelligence, Qilu University of Technology, where she contributes extensively to medical image processing and artificial intelligence research. She earned her doctorate in control science and engineering from Beihang University in Beijing. Her scholarly influence is supported by citation metrics across major databases, including a Google Scholar record showing more than five hundred citations with strong h-index and i10-index performance, and Scopus-indexed publications appearing in highly ranked journals. Her body of work spans intelligent systems, biomedical signal processing, autonomous control, and deep learning-driven medical applications.

Publication Profile

Google Scholar

Education Background

Prof. Changfang Chen completed her doctoral education at Beihang University with a focus on control science and engineering, where she developed a strong foundation in computational modeling, signal processing, and intelligent system design. Her academic journey fostered a multidisciplinary orientation that later supported her transition into artificial intelligence and medical image analysis. Through advanced coursework, laboratory research, and thesis contributions, she established technical strengths aligned with both theoretical control frameworks and practical biomedical computation, enabling a seamless integration of engineering principles with data-driven medical research applications.

Professional Experience

Prof. Changfang Chen serves as an associate professor at the Shandong Institute of Artificial Intelligence within Qilu University of Technology, contributing to research, postgraduate supervision, and high-impact project development. She has participated in multiple government-supported research programs, including national-level and provincial-level scientific foundations, where her role involved developing algorithms for image analysis, signal denoising, and autonomous systems. Her professional activity extends to collaboration with multidisciplinary teams, publication in leading indexed journals, and engagement in editorial and reviewing tasks, reflecting her sustained commitment to academic service and scientific advancement.

Awards and Honors

Throughout her career, Changfang Chen has been recognized through her involvement in competitive national and provincial research programs, reflecting the scientific value and societal relevance of her contributions. Her patents, including work on wavelet-domain ECG noise elimination, demonstrate innovation in biomedical signal processing. Her publications in prestigious SCI and Scopus-indexed journals such as Neurocomputing, Knowledge-Based Systems, IEEE Transactions on Instrumentation and Measurement, and IEEE Transactions on Intelligent Transportation Systems indicate consistent scholarly excellence. Her citation achievements further validate the long-term influence and recognition of her contributions within the global research community.

Research Focus

Prof. Changfang Chen’s research centers on medical image processing, biomedical signal reconstruction, autonomous control, and artificial intelligence with emphasis on multitask learning and deep neural architectures. Her recent work includes the development of a multi-task consistency learning framework designed to optimize predictions from unlabeled clinical images by integrating segmentation, signed distance mapping, and reconstruction processes. She has also contributed substantially to ECG signal denoising, autonomous vehicle tracking control, and wavelet-based sparse representations. Her research approach blends theoretical rigor with applied innovation to address challenges in modern intelligent healthcare technologies.

Top Publications

Chen, C., Jia, Y., Shu, M., & Wang, Y. (2015). Hierarchical adaptive path-tracking control for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2900–2912. This article has been cited widely for its contribution to autonomous path-tracking control and has received strong scholarly recognition based on citation counts.

Shu, M., Yuan, D., Zhang, C., Wang, Y., & Chen, C. (2015). A MAC protocol for medical monitoring applications of wireless body area networks. Sensors, 15(6), 12906–12931. This publication is frequently cited for its relevance to wireless body area networks and medical monitoring technologies, contributing significantly to wearable-sensing research.

Liu, H., Zhou, S., Chen, C., Gao, T., & Xu, J. (2022). Dynamic knowledge graph reasoning based on deep reinforcement learning. Knowledge-Based Systems, 241, 108235. This work has received strong citation activity and is noted for integrating reinforcement learning with knowledge graph reasoning in intelligent systems.

Hou, Y., Liu, R., Shu, M., Xie, X., & Chen, C. (2023). Deep neural network denoising model based on sparse representation algorithm for ECG signal. IEEE Transactions on Instrumentation and Measurement, 72, 1–11. This article is widely referenced for advancing ECG denoising using deep learning and sparse representation methods.

Hou, Y., Liu, R., Shu, M., & Chen, C. (2023). An ECG denoising method based on adversarial denoising convolutional neural network. Biomedical Signal Processing and Control, 84, 104964. This study has gained citations for its novel adversarial architecture applied to biomedical signal enhancement and reconstruction.

Conclusion

Through her sustained engagement in advanced artificial intelligence research, high-quality publications, and participation in major national science programs, Changfang Chen has established a strong academic profile within the fields of biomedical computation and intelligent systems. Her contributions to medical imaging and signal analysis demonstrate both technical innovation and societal relevance, while her citation record across Google Scholar and Scopus underscores her scholarly influence. Her work continues to advance computational methodologies that support reliability, accuracy, and efficiency in healthcare-oriented artificial intelligence systems.

Assist. Prof. Dr. Jiaxin Li | Metasurfaces | Research Excellence Award

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Assist. Prof. Dr. Jiaxin Li | Metasurfaces | Research Excellence Award

Researcher | Wuhan University of Technology | China

Dr. Li Jiaxin is a researcher in the fields of optical metamaterials and nano-optics, currently working at the China Electric Power Research Institute (Institute of Metrology). Previously, Li obtained a Doctor of Engineering degree from Wuhan University, School of Electronic Information, specializing in Physical Electronics. Her research focuses on metasurfaces and multifunctional metadevices — aiming at micro-nano fabrication and advanced light-wave manipulation. Over the years she has authored/co-authored more than ten SCI articles in high-impact optics and materials-science journals, including a paper selected as an ESI Highly Cited Paper. According to her public profile at a scholarly portal, her publications number around 17, with over 300 citations.

Publication Profile

Google Scholar

Education Background

Li Jiaxin obtained her Doctor of Engineering degree under an integrated Master–Doctoral programme at Wuhan University, in the School of Electronic Information focusing on Physical Electronics. Upon completion of her PhD, she made a transition to professional research in metrology and applied optics, combining her academic training with engineering practice.

Professional Experience

After earning her doctorate, Li Jiaxin joined the China Electric Power Research Institute — Institute of Metrology in mid-2023, where she currently serves as an Intermediate Engineer. Prior to that, during 2018–2023 she was a doctoral candidate at Wuhan University, during which period she worked in research on metasurfaces and nano-optical devices. Her dual exposure to academic research and applied metrology places her at the interface of fundamental photonics and practical engineering implementation.

Awards and Honors

Li Jiaxin secured competitive funding early in her career: she led a project supported by the National Natural Science Foundation of China (Young Scientists Fund), another project under the China Postdoctoral Science Foundation (Special Funding), and received support under the Hubei Postdoctoral Cutting-Edge Talent Introduction Program. She also participated in national-level research endeavours including the National Key R&D Program of China and the National Defense Science and Technology 173 Project. To date, she holds eight authorized national invention patents.

Research Focus

Her research centers on metasurface-based micro-nano optical technologies. She investigates mechanisms for manipulating light waves via metasurfaces, with particular emphasis on multifunctional metadevices, advanced imaging, and tunable optoelectronic components. Her work combines design, fabrication, and functional demonstration of metamaterial-based lenses, holograms, encryption metasurfaces and dynamic nanophotonic devices, leveraging micro–nano fabrication processes to realize high-density, multifunctional optical elements. Her most recent work includes an electrically tunable metalens based on PEDOT:PSS.

Publication

Zhang, M., Sun, D., Zhang, S., Deng, L., Li, J., & Guan, J. (2025). Electrically Tunable Metalens Based on PEDOT:PSS. Micromachines, 16(12), 1341.

Conclusion

Dr. Li Jiaxin represents a new generation of photonics researchers who bridge advanced academic research on metasurfaces with practical, engineering-oriented applications. Her strong publication record, supported funding and patents show both scientific creativity and technological relevance. As she advances in her career at the China Electric Power Research Institute, her contributions are likely to further impact the development of compact, reconfigurable, and multifunctional optical devices.

Ms. Ifza Shad | Computer Vision | Research Excellence Award

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Ms. Ifza Shad | Computer Vision | Research Excellence Award

University of Central Punjab | Pakistan

Ms. Ifza Shad is a computer vision and artificial intelligence researcher whose work focuses on real-time object detection, medical image analysis, deep learning optimization, and multimodal perception models for complex environments. Her research integrates advanced machine learning architectures, including YOLO-based detectors, attention-driven fusion networks, and lightweight deep learning frameworks designed for resource-efficient deployment in dynamic real-world scenarios. She has contributed to cutting-edge studies in aquatic and surface litter detection, brain tumor diagnosis, protective workwear recognition, and driver-behavior monitoring systems, demonstrating a strong emphasis on safety, healthcare, and environmental sustainability. Her interdisciplinary approach merges computer vision, robotics, and large-scale data processing, allowing her to design algorithms that address challenges in automation, public health, and smart systems. She has authored impactful publications in reputable international journals indexed in Scopus and Web of Science, with her research widely cited and accessible on Google Scholar. Her scholarly record includes peer-reviewed articles, collaborative projects with international researchers, and contributions to academic seminars and conferences. She continues to advance innovative detection models and AI-driven solutions, aiming to enhance real-time decision support systems through robust, interpretable, and computationally efficient algorithms. Her research output reflects a growing citation count, supported by Scopus metrics, Google Scholar indices, and document-level analytics, emphasizing her active role in the global scientific community and her contribution to emerging intelligent systems.

Profile

ORCID

Featured Publications

Shad, I., Zhang, Z., Asim, M., Al-Habib, M., Chelloug, S. A., & Abd El-Latif, A. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences, 18(2), 101534.

Shad, I., Bilal, O., & Hekmat, A. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering & Biosciences, 7(3).

Hekmat, A., Zhang, Z., Khan, S. U. R., Shad, I., & Bilal, O. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control, 101, 107221.