Nawel Benchaabane | Medical Image Analysis | Research Excellence Award

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Dr. Nawel Benchaabane | Medical Image Analysis | Research Excellence Award

Dr Chef De Projects | Audensiel Technologies | France 

Dr. Nawel Benchaabane is a researcher at Audensiel Technologies, Paris, France, specializing in artificial intelligence for healthcare and medical decision support. Her research focuses on AI-driven gait analysis, medical image understanding, and visual question answering for clinical diagnosis. She has authored 2 Scopus-indexed publications, with 17 citations and an h-index of 1, reflecting early but growing scientific impact. Her work has been published in high-impact venues such as Scientific Reports, IEEE EMBS Conference, and Intelligent Systems with Applications. Through interdisciplinary collaborations between AI and medical domains, her research contributes to improved diagnosis, patient monitoring, and data-driven healthcare innovation with tangible societal benefits.

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Featured Publications

Ramzi Ayadi | Hardware and Acceleration for Computer Vision | Research Excellence Award

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Mr. Ramzi Ayadi | Hardware and Acceleration for Computer Vision | Research Excellence Award

Assistant Professor | Kairouan University | Tunisia

Dr. Ramzi Ayadi is an Assistant Professor specializing in reconfigurable computing and system architecture, with a focus on temporal partitioning, scheduling, and placement techniques for dynamically reconfigurable systems. He has authored over 15 peer-reviewed publications, accumulating more than 81 citations h index 6, reflecting the global impact of his research. His work emphasizes optimizing communication costs, design latency, and resource management in FPGA-based and programmable architectures. Dr. Ayadi has collaborated extensively with researchers including B. Ouni, A. Mtibaa, and M. Abid, contributing to advancements in both theoretical and applied computing. His research fosters more efficient, adaptive computing systems with significant implications for engineering and technology development worldwide.

 

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Featured Publications


A partitioning methodology that optimizes the communication cost for reconfigurable computing systems.

-International Journal of Automation and Computing. (2012). Cited By: 15

Partitioning and scheduling technique for run time reconfigured systems.

– International Journal of Computer Aided Engineering and Technology. (2011). Cited By: 11

Naourez Benhadj | Deep Learning | Excellence in Research

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Prof. Naourez Benhadj | Deep Learning | Excellence in Research

Associate Professor | Ecole Nationale d’Ingénieurs de Sfax | Tunisian

Dr. Naourez Benhadj is a researcher at the Ecole Nationale d’Ingénieurs de Sfax (ENIS), Tunisia, specializing in electric machines, PMSM design, hybrid/electric vehicle energy management, and intelligent optimization techniques. With 32 scientific publications, 243 citations, and an h-index of 9, he has contributed significantly to fault detection, finite-element modeling, and advanced optimization algorithms, including recent work on transformer-based solar power prediction and PMSM design using chaotic PSO. Collaborating with over 30 international co-authors, his research supports sustainable mobility, smart energy systems, and high-efficiency electric transportation, fostering technological advancement and environmental impact on a global scale.

 

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Featured Publications


Comparison of fuel consumption and emissions of two hybrid electric vehicle configurations.

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2018) Cited By: 4

Design simulation and realization of solar battery charge controller using Arduino Uno..

-International Conference on Sciences and Techniques of Automatic Control and Computer Engineering . (2017) Cited By: 21

Torque ripple and harmonic density current study in induction motor: Two rotor slot shapes.

– International Review on Modelling and Simulations.(2007). Cited By: 5

Thermal modeling of permanent magnet motor with finite element method.

– International Conference on Sciences and Techniques of Automatic Control and Computer Engineering. (2014). Cited By: 5

Qi Lai | Medical Image Analysis | Women Researcher Award

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Dr. Qi Lai | Medical Image Analysis | Women Researcher Award

Assistant Professor | Shenzhen Institutes of Advanced Technology | China

Dr. Qi Lai is a researcher at the Shenzhen Institutes of Advanced Technology, China, specializing in weakly supervised learning, medical image analysis, and multi-instance learning. He has authored 11 peer-reviewed publications, receiving over 52 citations with an h-index of 5. His work spans deep learning for pathology, object detection, semantic segmentation, and medical image restoration, often in collaboration with international teams across leading institutions. Notable contributions include interactive and hybrid MIL frameworks and contrastive learning methods that enhance diagnostic precision. His research advances reliable AI-driven clinical decision support, contributing to improved healthcare technologies and societal well-being.

 

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Featured Publications


Fast broad multiview multi-instance multilabel learning (FBM3L) with viewwise intercorrelation.

– IEEE Transactions on Neural Networks and Learning Systems (2024). Cited By: 5 

Interactive multiple instance learning network for whole slide image analysis.

– Expert Systems with Applications. (2026). Cited By:  1

Joint discriminative latent subspace learning for image classification

– IEEE Transactions on Circuits and Systems for Video Technology. (2022). Cited By:: 15

Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

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Dr. Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

Jilin University of Chemical Technology | China

Dr. Yanli Shi is a researcher at the Jilin Institute of Chemical Technology, Jilin, China, with recognized contributions in image processing, computer vision, and intelligent information technologies. As a first author, Dr. Shi has published nearly 20 high-quality SCI and EI-indexed journal articles, including three papers in JCR Zone 1 journals, reflecting strong research impact and international visibility. According to Scopus, Dr. Shi’s work has received 160 citations, with an h-index of 7, demonstrating consistent scholarly influence.Dr. Shi has led and successfully completed several competitive research projects, including one project funded by the Jilin Provincial Natural Science Foundation, one project under the “13th Five-Year Plan” Science and Technology Program of the Jilin Provincial Department of Education, and one vertical project supported by the Jilin Municipal Science and Technology Bureau, which also included the Outstanding Young Talent Cultivation Program. These projects have advanced both fundamental research and applied technological development.With a strong emphasis on technology transfer and practical innovation, Dr. Shi holds one national invention patent and has actively translated research outcomes into industrial solutions. Through extensive collaboration, Dr. Shi has participated in over 100 horizontal projects with Inner Mongolia University and local enterprises, generating more than 1.6 million yuan in research funding. These collaborations have addressed real-world technical challenges and promoted regional industrial and technological development.Dr. Shi’s recent publications in leading journals such as Pattern Recognition and Scientific Reports further highlight expertise in fine-grained visual classification, deep learning, and image super-resolution. Overall, Dr. Shi’s work demonstrates a balanced integration of academic excellence, cross-sector collaboration, and measurable societal and economic impact.

Profile: Scopus 

Featured Publications

1.Shi, Y., et al. (2025). Multi-scale adversarial diffusion network for image super-resolution. Scientific Reports.  Cited By: 1

2.Shi, Y., et al. (2025). LDH-ViT: Fine-grained visual classification through local concealment and feature selection. Pattern Recognition. Cited By : 1

Dr. Yanli Shi research advances state-of-the-art computer vision and image intelligence technologies, bridging fundamental algorithms with real-world industrial applications. Through high-impact publications, patented innovations, and extensive university–industry collaborations, the work delivers scalable solutions to practical technical challenges. This integration of scientific excellence and technology transfer contributes meaningfully to societal development and global innovation.

Mueen Uddin | Medical Image Analysis | Research Excellence Award

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Prof. Dr. Mueen Uddin | Medical Image Analysis | Research Excellence Award

Professor | University Of Doha For Science and Technology | Qatar

Dr. Mueen Uddin is an Associate Professor of Cybersecurity and Data Sciences at the University of Doha for Science & Technology (UDST), Qatar. He is an internationally recognized researcher whose work bridges cybersecurity, blockchain technologies data science artificial intelligence, and healthcare security. His scholarly contributions reflect a strong commitment to advancing secure, intelligent, and sustainable digital systems across multidisciplinary domains.Dr. Uddin has authored over 192 peer-reviewed research publications in leading international journals and conferences, including IEEE Access, IEEE Network Renewable  Sustainable Energy Reviews, Sustainability and Health Informatics Journal. His research impact is evidenced by more than 7,404 citations an h-index of 43, and an i10-index of 99, underscoring the consistency quality and global relevance of his work. Several of his publications are widely cited benchmarks particularly in handwritten OCR systems medical image segmentation, blockchain for healthcare and digital twins energy-efficient data centers and IoT-enabled cybersecurity infrastructures.His research expertise spans Blockchain and Web 3.0, IoT and Cybersecurity Healthcare Security Metaverse technologies Deep Learning and Green IT systems. Dr. Uddin has played a pivotal role in advancing blockchain-based drug traceability solutions secure electronic health records intrusion detection systems and AI-driven healthcare analytics contributing directly to combating counterfeit drugs enhancing patient data security and improving diagnostic intelligence.Dr. Uddin actively collaborates with researchers across Asia Europe the Middle East and Africa fostering interdisciplinary and cross-border research initiatives. These collaborations have resulted in impactful studies addressing real-world challenges in smart cities sustainable development healthcare digitalization and intelligent network security.Beyond academia his work demonstrates strong societal and industrial relevance offering scalable secure solutions aligned with global priorities such as digital trust, sustainable computing, and resilient healthcare systems. Through high-impact research, academic leadership, and global collaboration Dr. Mueen Uddin continues to shape the future of cybersecurity and data-driven innovation worldwide.

Profiles: Scopus | ORCID | Googlescholar 

Featured Publications

1.Memon, J., Sami, M., Ahmed, R., & Uddin, M. (2020). Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR). IEEE Access, 8, 142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542. Cited By : 730

2.Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rad, A. E., Rehman, A., & Uddin, M. (2014). Medical image segmentation methods, algorithms, and applications. IETE Technical Review, 31(3), 199–213. https://doi.org/10.1080/02564602.2014.906861. Cited By : 440

3.Uddin, M., & Rahman, A. A. (2012). Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics. Renewable and Sustainable Energy Reviews, 16(6), 4078–4094. https://doi.org/10.1016/j.rser.2012.03.002. Cited By : 318

4.Yaqoob, M. I. I., Salah, K., Uddin, M., Jayaraman, R., & Omar, M. (2020). Blockchain for digital twins: Recent advances and future research challenges. IEEE Network, 34(5), 290–298. https://doi.org/10.1109/MNET.2020.9225779. Cited By :  291

5.Uddin, M. (2021). Blockchain MedLedger: Hyperledger Fabric–enabled drug traceability system for counterfeit drugs in pharmaceutical industry. International Journal of Pharmaceutics, 597, 120235. https://doi.org/10.1016/j.ijpharm.2021.120235. Cited By : 273

Dr. Mueen Uddin’s research advances global innovation by integrating cybersecurity, blockchain, and AI to build secure, trustworthy, and sustainable digital ecosystems. His work delivers high-impact solutions for healthcare security, smart infrastructure, and data-intensive systems, translating scientific excellence into real-world societal and industrial benefits worldwide.

Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

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Mr. Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

Research Assistant | Daffodil International University | Bangladesh

Mr. Abu Hanzala Daffodil International University, Dhaka, BangladeshHanzala, Abu is an emerging researcher specializing in artificial intelligence–driven medical image analysis, deep learning, and explainable healthcare systems. The researcher’s scholarly work focuses on developing robust hybrid and ensemble learning frameworks that integrate convolutional neural networks (CNNs), vision transformers (ViTs), graph neural networks (GNNs), transfer learning, self-supervised learning, and attention mechanisms for disease detection and classification.A key research achievement includes the publication of a peer-reviewed article in Array (2025) titled “A Hybrid Approach for Cervical Cancer Detection: Combining D-CNN, Transfer Learning, and Ensemble Models”, which demonstrates improved diagnostic accuracy using advanced ensemble strategies. In addition, the researcher has several manuscripts under peer review in high-impact international journals including Scientific Reports Neuroscience, IEEE Transactions on Medical Imaging, ACM Transactions on Computing for Healthcare, Discover Applied Science and Computers & Education: Artificial Intelligence. These studies address a wide range of clinically significant problems such as cervical, lung, and colorectal cancer, Alzheimer’s disease pneumonia neuromuscular disorders peripheral nerve disease and cerebral cortex pathology.The researcher has authored 5 scholarly documents receiving 5 citations, and currently holds an h-index of 2, reflecting a growing academic impact within the medical AI research community. International visibility is further strengthened through a peer-reviewed IEEE conference paper and an invited oral presentation at the 15th International Conference on Computing Communication and Networking Technologies (ICCCNT 2024).Research collaborations span multidisciplinary teams involving computer scientists biomedical engineers and healthcare researchers. The societal impact of this work lies in advancing early disease detection reliable clinical decision support and explainable AI models contributing to scalable trustworthy and globally relevant healthcare technologies.

Profiles: Scopus | ResearchGate

Featured Publication

1. Hanzala, A., Akter, T., & Rahman, M. S. (2025). A hybrid approach for cervical cancer detection: Combining D-CNN, transfer learning, and ensemble models. Cited By : 3

Mr. Abu Hanzala research advances global healthcare innovation by integrating reliable, explainable artificial intelligence with medical imaging to enable early disease detection and data-driven clinical decision support. This work bridges scientific rigor and real-world applicability, contributing to scalable, trustworthy AI solutions with meaningful societal and clinical impact.

Tian Gao | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award

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Dr. Tian Gao | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award

The Information Engineering University | China

Dr. Tian Gao is a distinguished researcher in the field of remote sensing, specializing in multimodal image matching, Arctic sea ice motion analysis, and image registration for optical and SAR imagery. He completed his graduate studies at PLA Information Engineering University, Zhengzhou, China, focusing on geospatial information and advanced computational methods for Earth observation.Gao has authored 11 peer-reviewed publications, including in top-tier journals such as IEEE Sensors Journal, ISPRS Journal of Photogrammetry and Remote Sensing, and the International Journal of Applied Earth Observation and Geoinformation. His notable contributions include the development of SFA-Net, a SAM-guided focused attention network for multimodal remote sensing image matching, and innovative approaches to sharpened side phase fusion and self-similar adjacent self-convolutional feature registration. Gao’s work also encompasses keypoint-free feature tracking for Arctic sea ice motion retrieval, DEM super-resolution using attention-based and relative depth-guided methods, and GNSS-denied UAV geolocalization. These efforts have advanced both methodological innovation and practical applications in environmental monitoring, geospatial intelligence and disaster response.His research demonstrates extensive collaboration with domestic and international scholars, reflecting interdisciplinary engagement across remote sensing, UAV imaging, and geospatial data analysis. Gao’s publications have collectively received 51 citations, highlighting the growing impact of his work in the scientific community.Beyond methodological contributions Gao’s work has significant societal and environmental relevance enabling improved monitoring of polar ice dynamics, enhancing emergency response through UAV-assisted image stitching and supporting sustainable geospatial intelligence applications. With expertise spanning optical and SAR imagery multimodal data fusion and image registration, Tian Gao continues to contribute to cutting-edge research that bridges academic innovation with real-world solutions in Earth observation and remote sensing.

Profiles: ORCID | Scopus

Featured Publications

1.Wang, Y., Lan, C., Gao, T., Yao, F., & Mu, Z. (2025). Multimodal image matching using sharpened side phase fusion method. IEEE Sensors Journal.

2.Gao, T., Lan, C., Lv, L., Shi, Q., Huang, W., Wang, Y., & Mu, Z. (2025). Robust registration of multimodal remote sensing images using self-similar adjacent self-convolutional feature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

3.Gao, T., Lan, C., Zhou, C., Zhang, Y., Huang, W., Wang, L., & Wang, Y. (2025). Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm. ISPRS Journal of Photogrammetry and Remote Sensing.  Cited By: 1

4.Huang, W., Sun, Q., Guo, W., Xu, Q., Wen, B., Gao, T., & Yu, A. (2025). Multi-modal DEM super-resolution using relative depth: A new benchmark and beyond. International Journal of Applied Earth Observation and Geoinformation.

5.Gao, T., Lan, C., Huang, W., & Wang, S. (2025). SFA-Net: A SAM-guided focused attention network for multimodal remote sensing image matching. ISPRS Journal of Photogrammetry and Remote Sensing.

Tian Gao’s research advances remote sensing and multimodal image analysis, enabling precise monitoring of Arctic sea ice, GNSS-denied UAV navigation, and environmental changes. His work bridges scientific innovation with practical applications, supporting disaster response, geospatial intelligence, and sustainable environmental management globally.

Ahmed Elmekawy | Startups and Industry Applications | Research Excellence Award

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Dr. Ahmed Elmekawy | Startups and Industry Applications | Research Excellence Award

Researcher | Saint Petersburg State University | Egypt

Dr. Ahmed Hassan Abdelrahman Elmekawy is a researcher in Condensed Matter Physics, specializing in magnetic nanowires, FORC (First-Order Reversal Curve) analysis, micromagnetic modeling, and nanoscale magnetism. He is affiliated with JINR, St. Petersburg State University, and the Cyclotron Project at the Egyptian Atomic Energy Authority (EAEA). His work bridges theoretical modeling and advanced experimental techniques for understanding magnetic behavior in low-dimensional nanostructures.Dr. Elmekawy has authored 11 scientific publications, accumulating 71 citations, with an h-index of 4 and an i10-index of 3, reflecting his growing visibility and influence in nanomagnetism research. His contributions focus on unraveling magnetization dynamics internal magnetic interactions and structural property relationships in iron and Ni/Cu nanowire arrays which are foundational materials for next-generation spintronic devices magnetic sensors and energy-efficient data storage systems.Among his notable works his publication Magnetic Properties and FORC Analysis of Iron Nanowire Arrays stands as a highly cited study that advanced the interpretation of magnetic interactions through FORC techniques. His subsequent studies including “Magnetic Properties of Ordered Arrays of Iron Nanowires: The Impact of Length and Effect of Interactions and Non-uniform Magnetic States on Magnetization Reversal  further deepened scientific understanding of nanoscale magnetism and geometrical effects on magnetization reversal mechanisms.His recent publications in Nano-Structures & Nano-Objects and Journal of Magnetism and Magnetic Materials highlight significant advancements in correlating FORC measurements with micromagnetic simulations demonstrating compatibility between theoretical modeling and experimental observations. These studies provide new frameworks for evaluating internal magnetic interactions in segmented and non-segmented nanowire systems offering new tools for material optimization.In addition to nanomagnetism Dr. Elmekawy has contributed to nuclear physics particularly through work on proton and antiproton scattering from He using Glauber multiple scattering models. His interdisciplinary collaborations span Russia, Egypt, and Europe, showcasing strong international engagement.Dr. Elmekawy’s research contributes to societal and technological innovation by supporting the development of advanced magnetic materials crucial for secure communication systems biomedical imaging energy systems and miniaturized electronic components. His scientific trajectory reflects a commitment to precision collaboration and impactful discovery.

Profile: Googlescholar

Featured Publications

1.Elmekawy, A. H. A., Iashina, E. G., Dubitskiy, I. S., Sotnichuk, S. V., & Bozhev, I. V., et al. (2020). Magnetic properties and FORC analysis of iron nanowire arrays. Materials Today Communications, 25, 101609.  Cited By: 26

2.Elmekawy, A. H. A., Iashina, E., Dubitskiy, I., Sotnichuk, S., Bozhev, I., & Kozlov, D., et al. (2021). Magnetic properties of ordered arrays of iron nanowires: The impact of the length. Journal of Magnetism and Magnetic Materials, 532, 167951. Cited By: 20

3.Dubitskiy, I. S., Elmekawy, A. H. A., Iashina, E. G., Sotnichuk, S. V., & Napolskii, K. S., et al. (2021). Effect of interactions and non-uniform magnetic states on the magnetization reversal of iron nanowire arrays. Journal of Superconductivity and Novel Magnetism, 34(2), 539–549. Cited By: 16

4.Mistonov, A. A., Dubitskiy, I. S., Elmekawy, A. H. A., Iashina, E. G., & Sotnichuk, S. V., et al. (2021). Change in the direction of the easy magnetization axis of arrays of segmented Ni/Cu nanowires with increasing Ni segment length. Physics of the Solid State, 63(7), 1058–1064. Cited By: 7

5.Nabiyev, A. A., Mustafayev, I. I., Mehdiyeva, R. N., Nuriyev, M. A., & Andreev, E. V., et al. (2025). Post‐γ‐irradiation effects in nano-SiO2 particle reinforced high-density polyethylene composite films: Structure–property relationships, thermal stability, and degradation. Polymer Composites. Cited By: 1

Dr. Ahmed Elmekawy’s research in magnetic nanowires and FORC analysis advances fundamental understanding of nanoscale magnetism, enabling innovations in spintronics, high-density data storage, and energy-efficient magnetic devices. His interdisciplinary work bridges theory and experiment, contributing to technological development, materials science, and global scientific progress.

Jinxu Zhang | Document Image Analysis | Research Excellence Award

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Dr. Jinxu Zhang | Document Image Analysis | Research Excellence Award

Harbin Institute of Technology | China

Dr. Jinxu Zhang is a researcher at the Harbin Institute of Technology specializing in multimodal understanding, Document Visual Question Answering (DocVQA), and multimodal large language models. His work focuses on advancing key technologies for interpreting complex, multi-form, and multi-page documents, contributing significantly to the fields of document intelligence and machine reading systems.He has completed and continues to contribute to the National Natural Science Foundation of China (NSFC) project on Key Technologies of Multi-form Document VQA. His research outputs include six SCI/Scopus-indexed publications5 , with a total of 41 citations, an h-index of 2, and an i10-index of 2. His contributions appear in top-tier venues such as ACM Multimedia (CCF-A), EMNLP Findings (CCF-B), Information Fusion (SCI, IF 15.5), and IEEE Transactions on Learning Technologies. His notable works CREAM, DocRouter, DocAssistant, and DREAM introduce innovative solutions for hierarchical multimodal retrieval, prompt-guided vision transformers, mixture-of-experts connectors and robust reasoning strategies for document comprehension.Dr. Zhang’s patented work on an intelligent question-answering system for multi-form documents further extends his impact toward practical deployable intelligent document systems. His research achievements emphasize coarse-to-fine retrieval key-region reading step-wise reasoning and efficient multimodal fusion. He also incorporates Reinforcement Learning–based data enhancement and Chain-of-Thought (CoT) construction to improve model reasoning in multi-page document analysis.He actively collaborates with university researchers in multimodal understanding document analysis OCR and deep learning fostering interdisciplinary innovation. His work contributes to building reliable and generalizable document intelligence systems with broad societal applications including education digital governance business automation and large-scale knowledge management.Dr. Zhang continues to advance the frontier of intelligent document analysis through sustained research model innovation and high-impact scholarly contributions.

Profiles: Scopus |  Googlescholar

Featured Publications

1.Liu, M., Zhang, J., Nyagoga, L. M., & Liu, L. (2023). Student-AI question cocreation for enhancing reading comprehension. IEEE Transactions on Learning Technologies, 17, 815–826. Cited By: 28

2.Zhang, J., Yu, Y., & Zhang, Y. (2024). CREAM: Coarse-to-fine retrieval and multi-modal efficient tuning for document VQA. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 925–934). Cited By:  13

3.Zhang, J., Fan, Q., & Zhang, Y. (2025). DocAssistant: Integrating key-region reading and step-wise reasoning for robust document visual question answering. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 3496–3511).

4.Zhang, J., Fan, Q., Yu, Y., & Zhang, Y. (2025). DREAM: Integrating hierarchical multimodal retrieval with multi-page multimodal language model for documents VQA. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 4213–4221).

5.Zhang, J., & Zhang, Y. (2025). DocRouter: Prompt guided vision transformer and Mixture of Experts connector for document understanding. Information Fusion, 122, Article 103206.

Dr. Zhang’s research advances the global frontier of intelligent document understanding by enabling machines to accurately interpret complex, multi-page documents with human-level reasoning. His innovations in multimodal fusion, retrieval, and robust VQA architectures support breakthroughs in scientific research, digital governance, education, and automated knowledge management. Ultimately, his work drives the development of reliable, scalable, and socially beneficial AI systems that enhance information accessibility and decision-making worldwide.