Sanghyun Lee | Automotive Design Agency Development | Best Researcher Award

Published on by Computer Vision Awards

Dr. Sanghyun Lee | Automotive Design Agency Development | Best Researcher Award

Senior Research Enginner | Hyundai Motor Company | South Korea

Dr. Sanghyun Lee is a senior research engineer at Hyundai Motor Company in the Advanced Vehicle Platform division, specializing in intelligent technology–based vehicle body design, closure mechanisms, sealing systems, and AI-driven design automation. He earned his bachelor’s degree in Mechanical Engineering from Korea University and is currently pursuing a combined MS/Ph.D. in Mechanical Engineering at Sungkyunkwan University. His professional experience spans more than two decades, including door and closure mechanism engineering, sealing system design leadership, and new mobility concept development with integrated intelligent technologies. His research interests focus on ontology-based knowledge graphs, RAG systems, generative and parametric design, and CAD automation applied to advanced automotive systems. He has contributed to several impactful industry and academic projects, collaborating with Yonsei University on knowledge graph with RAG systems and with KAIST on generative tailgate design. His work is widely recognized through more than thirty patents across multiple countries and several publications in reputed international journals such as Materials Today Communications, Advanced Engineering Informatics, and the International Journal of Automotive Technology. He has also contributed to advancing intelligent design frameworks that integrate AI and human-in-the-loop knowledge curation to accelerate automotive innovation. He is a member of the Korean Society of Automotive Engineers and has played a vital role in bridging industrial research with academic progress. His research profile currently reflects 4 citations, 1 document, and an h-index of 1.

Profiles: Google Scholar | Scopus | LinkedIn

Featured Publications

  1. Akay, H., Lee, S. H., & Kim, S. G. (2023). Push-pull digital thread for digital transformation of manufacturing systems. CIRP Annals, 72(1), 401–404.

  2. Shim, M., Choi, H., Koo, H., Um, K., Lee, K. H., & Lee, S. (2025). OmEGa (Ω): Ontology-based information extraction framework for constructing task-centric knowledge graph from manufacturing documents with large language model. Advanced Engineering Informatics, 64, 103001.

  3. Lee, S. H., Yoon, B., Cho, S., Lee, S., Hong, K. M., & Suhr, J. (2023). Multidisciplinary design of door inner belt weatherstrip for simultaneous reduction of wind noise and squeaking in electric vehicles. Materials Today Communications, 37, 107567.

  4. Lee, S. H., Yoon, B., Kwon, H., Seo, C. M., & Suhr, J. (2025). Design optimization for minimizing performance deviations of complex vehicle door systems using virtual manufacturing big data and axiomatic design. International Journal of Automotive Technology, 26(4), 1–25.

  5. Lee, S., Kim, M. K., Kim, M., Hong, K. M., & Suhr, J. (2025). Multidisciplinary tailgate guide bumper design for electric vehicles: Overcoming rattle, separation noise and closure effort trade-offs. Materials Today Communications, 112593.

Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Published on by Computer Vision Awards

Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Karim Dabbabi | Unmanned Aerial Vehicle Tracking | Best Academic Researcher Award

Published on by Computer Vision Awards

Dr. Karim Dabbabi | Unmanned Aerial Vehicle Tracking | Best Academic Researcher Award

Assistant Professor | Faculty of Sciences of Tunis | Tunisia

Dr. Karim Dabbabi is an accomplished researcher and assistant professor specializing in artificial intelligence, computer vision, natural language processing, and biomedical signal processing. He holds a Ph.D. in Electronics from the Faculty of Sciences of Tunis and has completed advanced studies in automation, signal processing, and embedded electronics. His professional experience includes teaching courses in image and signal processing, machine and deep learning, AI, coding languages, embedded systems, and IoT across multiple institutions, as well as mentoring and supervising numerous student theses. He has actively contributed to national and international research projects, including UAV real-time tracking, smart grids, COVID-19 patient monitoring, intelligent wheelchairs, and disaster management robotics. His research interests focus on multimodal speech and image analysis, intelligent systems, emotion-aware speech recognition, and healthcare applications such as early detection of Parkinson’s and Alzheimer’s diseases. He has published extensively in reputed journals and conferences, including IEEE, Scopus, and Springer, and holds certifications in AI, machine learning, embedded systems, and data science. His work reflects strong leadership in research supervision, active involvement in academic communities, and commitment to advancing technology for societal benefit. His research skills encompass programming (Python, MATLAB, C, C++, Java, VHDL), machine learning frameworks, embedded systems, and IoT development. Dr. Dabbabi’s contributions are evidenced by 39 citations, 17 documents, and an h-index of 4.

Profiles: Google Scholar | Scopus | ResearchGate 

Featured Publications

  1. Dabbabi, K., Hajji, S., & Cherif, A. (2020). Real-time implementation of speaker diarization system on Raspberry PI3 using TLBO clustering algorithm. Circuits, Systems, and Signal Processing, 39(8), 4094–4109.

  2. Walid, M., Bousselmi, S., Dabbabi, K., & Cherif, A. (2019). Real-time implementation of isolated-word speech recognition system on Raspberry Pi 3 using WAT-MFCC. International Journal of Computer Science and Network Security, 19(3), 42.

  3. Dabbabi, K., Kehili, A., & Cherif, A. (2023). Parkinson detection using VOT-MFCC combination and fully-connected deep neural network (FC-DNN) classifier. In Proceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET 2023).

  4. Dabbabi, K., Delleji, T. (2025). Graph neural network-tracker: A graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking. Visual Computing for Industry, Biomedicine, and Art, 8(1), 18.

  5. Dabbabi, K., Mars, A. (2024). Self-supervised learning for speech emotion recognition task using audio-visual features and Distil Hubert model on BAVED and RAVDESS databases. Journal of Systems Science and Systems Engineering, 33(5), 576–606.

Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Published on by Computer Vision Awards

Prof. Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Dean of Computer Science and Artificial Intelligence | Wuhan Textile University | China

Prof. Xinrong Hu is a distinguished researcher and academic leader in computer vision, natural language processing, virtual reality, and machine learning. She serves as Dean of the School of Computer and Artificial Intelligence at Wuhan Textile University and is a doctoral supervisor, leading an innovative research team at the Hubei Provincial Engineering Technology Research Center for Garment Informatization. She holds a Ph.D. and has extensive experience in guiding research projects, including over 30 funded initiatives, some with national and international significance. Her research interests focus on advancing artificial intelligence applications in real-world scenarios, combining theoretical innovation with practical solutions. She has authored more than 100 academic papers, edited six textbooks, translated a book, and holds 26 invention patents, demonstrating her strong research skills and contribution to knowledge dissemination. Prof. Hu has been recognized with multiple awards and honors, including provincial and ministerial-level scientific research awards, teaching achievement awards, and prestigious titles such as Hubei Provincial Distinguished Teacher and recipient of the Special Government Allowance from the State Council. Her professional engagement includes leadership in academic communities, mentorship of young researchers, and active participation in advancing the field of AI through both education and research initiatives. Her comprehensive expertise, innovative contributions, and dedication to fostering academic excellence make her a leading figure in her field. Her research impact is reflected in 1,044 citations, 209 documents, and an h-index of 16.

Profiles: Scopus | ResearchGate 

Featured Publications

  1. Hu, X., et al. (2025). CDPMF-DDA: Contrastive deep probabilistic matrix factorization for drug-disease association prediction. BMC Bioinformatics.

  2. Hu, X., et al. (2025). Source-free cross-modality medical image synthesis with diffusion priors. Journal of King Saud University – Computer and Information Sciences.

  3. Hu, X., et al. (2025). TADUFMA: Transformer-based adaptive denoising and unified feature modeling for multi-condition anomaly detection in computerized flat knitting machines. Measurement Science and Technology.

  4. Hu, X., et al. (2025). ViT-BF: Vision transformer with border-aware features for visual tracking. Visual Computer.

  5. Hu, X., et al. (2025). Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics.