Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

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Dr. Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

Doctor | Beijing University of Posts and Telecommunications | China

Dr. Boyuan Bai is an emerging researcher in advanced visual computing, with a focused contribution to 3D reconstruction, Gaussian Splatting, multi-view scene modeling, and uncertainty-aware machine learning. His work integrates computer graphics, deep learning, and computational geometry to develop intelligent systems capable of producing highly accurate and stable indoor scene reconstructions. With 83 citations, 4 Scopus-indexed publications, and an h-index of 3, he is rapidly establishing a strong research footprint. Dr. Boyuan Bai’s notable scientific contribution centers on UncertainGS, an uncertainty-aware indoor reconstruction framework published in Neurocomputing (SCI/Scopus). This research introduces a novel pipeline that integrates cross-modal uncertainty prediction to guide the optimization of Gaussian Splatting. His methodological innovation improves the fidelity of reconstructed surfaces, especially in textureless or geometrically ambiguous indoor regions. His incorporation of Manhattan-world constraints into the Gaussian Splatting process represents a significant leap forward in aligning 3D surface geometry with real-world structural patterns. His research areas broadly span multi-view 3D reconstruction, Gaussian Splatting, uncertainty modeling, scene understanding, and deep reinforcement learning for geometric perception. He actively contributes to the development of next-generation 3D vision technologies, with applications in robotics, digital twins, AR/VR environments, and autonomous spatial intelligence. His work shows strong potential for large-scale deployment in real-time virtual reconstruction and simulation systems. Dr. Boyuan Bai’s scholarly output includes peer-reviewed journal publications, research project leadership, and scientific contributions that address fundamental challenges in computational imaging. His research achievements demonstrate clear innovation, technical depth, and growing influence in the fields of computer vision and graphics. Through ongoing academic collaborations and continued focus on high-impact research problems, he is emerging as a promising researcher in intelligent 3D scene modeling and uncertainty-aware visual computing.

Profiles: Scopus | IEEE Xplore | ACM Digital Library 

Featured Publications

1. Bai, B., Qiao, X., Lu, P., Zhao, H., Shi, W., & others. (2025). Two grids are better than one: Hybrid indoor scene reconstruction framework with adaptive priors. Neurocomputing, 618(C). https://doi.org/10.1016/j.neucom.2024.129118

2. Huang, Y., Bai, B., Zhu, Y., Qiao, X., Su, X., Yang, L., & others. (2024). ISCom: Interest-aware semantic communication scheme for point cloud video streaming on Metaverse XR devices. IEEE Journal on Selected Areas in Communications, 42(4). https://doi.org/10.1109/JSAC.2023.3345430

3. Zhu, Y., Huang, Y., Qiao, X., Tan, Z., Bai, B., & others. (2023). A semantic-aware transmission with adaptive control scheme for volumetric video service. IEEE Transactions on Multimedia, 25. https://doi.org/10.1109/TMM.2022.3217928

4. Huang, Y., Zhu, Y., Qiao, X., Tan, Z., & Bai, B. (2021). AITransfer: Progressive AI-powered transmission for real-time point cloud video streaming. In Proceedings of the 29th ACM International Conference on Multimedia (MM ’21). https://doi.org/10.1145/3474085.3475624

Wei Pan | Artificial Intelligence | Best Researcher Award

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Dr. Wei Pan | Artificial Intelligence | Best Researcher Award

Researcher | OPT Machine Vision | Japan

Dr. Wei Pan is an accomplished researcher specializing in machine vision, 3D imaging, computational geometry, and optical metrology, currently contributing to OPT Machine Vision Corporation in Japan. His research is positioned at the intersection of machine learning, geometric learning, and computer-aided design, with applications in precision manufacturing, intelligent inspection, and automation. With 26 Scopus-indexed publications, 146 citations, and an h-index of 7, Dr. Wei Pan’s research has advanced computational methodologies for 3D reconstruction, point cloud processing, mesh denoising, phase-shifting profilometry, and surface metrology. His works have appeared in leading journals including Advanced Photonics, Optics Express, Computer-Aided Design, Automation in Construction, and The Visual Computer. Notably, his 2024 publications explore deep-learning-embedded structured light imaging and topology-aware transformers for point cloud registration, reflecting his pioneering integration of AI and optical engineering. Dr. Wei Pan has demonstrated exceptional innovation through 39 patents across domains such as 3D data filtering, surface defect detection, structured light reconstruction, and intelligent robotic calibration. These inventions strengthen industrial imaging precision and automation efficiency. His patent WO-2022057250-A1 on mesh denoising and CN-118397020-A on image segmentation and contour extraction exemplify impactful R&D contributions to intelligent vision systems. Beyond publications and patents, Dr. Wei Pan actively engages in collaborative research and R&D leadership, driving algorithmic innovation in structured-light metrology and computer vision. His research excellence has been recognized with multiple distinctions, including the President’s Graduate Fellowship (Singapore) and the Kuang-Chi Young Talents Award (China). Through his interdisciplinary expertise bridging optical design, machine learning, and computational modeling, Dr. Wei Pan continues to advance the frontiers of intelligent manufacturing and vision-based automation technologies.

Profiles: Scopus | ORCID | Google Scholar | ResearchGate

Featured Publications

  • Liu, J., Hao, J., Lin, H., Pan, W., Yang, J., Feng, Y., Wang, G., Li, J., Jin, Z., Zhao, Z., & Liu, Z. (2023). Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction. Patterns, 4(9), Article 100953.

  • Lu, L., Bu, C., Su, Z., Guan, B., Yu, Q., Pan, W., & Zhang, Q. (2024). Generative deep-learning-embedded asynchronous structured light for three-dimensional imaging. Advanced Photonics, 6(4), 046004–046004.

  • Chen, S., Wang, J., Pan, W., Gao, S., Wang, M., & Lu, X. (2023). Towards uniform point distribution in feature-preserving point cloud filtering. Computational Visual Media, 9(2), 249–263.

  • Lu, L., Jia, Z., Pan, W., Zhang, Q., Zhang, M., & Xi, J. (2020). Automated reconstruction of multiple objects with individual movement based on PSP. Optics Express, 28(18), 28600–28611.

  • Si, G. Y., Leong, E. S. P., Pan, W., Chum, C. C., & Liu, Y. J. (2014). Plasmon-induced transparency in coupled triangle-rod arrays. Nanotechnology, 26(2), 025201.

 

Jinfu Fan | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Jinfu Fan | Machine Learning | Best Researcher Award

Professor at Qingdao University, China.

Assoc. Prof. Dr. Jinfu Fan is a dedicated researcher and academic currently serving as an Associate Professor at the College of Computer Science Technology, Qingdao University (QU), China. He earned his Ph.D. in Computer Science from Tongji University, Shanghai, and further expanded his academic horizons as a visiting scholar at the School of Computing (SoC), National University of Singapore (NUS), from January to December 2022. Dr. Fan specializes in the fields of machine learning, data mining, and computer vision, with a notable emphasis on weakly supervised multi-label learning and super-resolution image reconstruction. His recent research project, MDiffSR, explores the application of mutual information and diffusion models in image super-resolution and has been published in the renowned Neurocomputing journal. His scholarly work demonstrates a solid blend of theoretical foundations and practical innovation, offering new directions in data-efficient learning and visual computing. Beyond research, Dr. Fan is actively involved in curriculum development and student mentorship at QU, contributing significantly to academic growth and collaborative learning. His passion for integrating advanced algorithms with real-world applications marks him as a progressive thinker and an impactful contributor in the field of artificial intelligence and computational imaging.

Publication Profile

Scopus

Orcid

Google  Scholar

Educational Details

Dr. Jinfu Fan obtained his Ph.D. degree in Computer Science from Tongji University, Shanghai, one of China’s leading research universities known for its engineering and technological disciplines. During his doctoral training, he focused on advanced learning algorithms and optimization techniques, gaining strong expertise in machine learning and data-driven modeling. His academic foundation combines rigorous computational training with a deep understanding of mathematical modeling, which laid the groundwork for his current research in weakly supervised learning and super-resolution technologies. In addition to his doctoral studies, Dr. Fan further honed his academic skills through a postdoctoral or visiting scholar tenure at the National University of Singapore (NUS), a globally recognized institution in computer science and AI research. This international experience provided him with broader research exposure, particularly in collaborative and cross-disciplinary projects, and allowed him to work alongside some of the field’s top minds. His educational background not only reflects technical depth but also a proactive approach toward lifelong learning and global academic engagement. His training in both national and international settings has helped cultivate a well-rounded understanding of computer science principles and their real-world applications, making him a versatile researcher and educator in the evolving tech landscape.

Professional Experience

Assoc. Prof. Dr. Jinfu Fan is currently affiliated with the College of Computer Science Technology at Qingdao University (QU), where he serves as an Associate Professor. In this role, he is actively involved in both research and teaching, guiding undergraduate and graduate students in areas related to machine learning, computer vision, and artificial intelligence. Prior to joining QU, he completed his Ph.D. at Tongji University and enriched his professional experience as a visiting scholar at the National University of Singapore (NUS) from January to December 2022. During his time at NUS, Dr. Fan collaborated with prominent researchers at the School of Computing, which broadened his research scope and provided him with valuable international experience. At Qingdao University, he has initiated and led several research projects, including the development of image super-resolution frameworks using mutual information and diffusion models. His role as an academic also includes curriculum development, research supervision, and interdisciplinary collaboration. His professional journey reflects a strong commitment to technological advancement and academic excellence, marked by his ability to integrate research with education and translate theory into practical innovations. Dr. Fan’s progressive academic and research track record underscores his dedication to scholarly leadership in AI and computational science.

Research Interest

Dr. Jinfu Fan’s research interests span across several cutting-edge areas within artificial intelligence and computer vision. He is particularly focused on machine learning and data mining, with a growing specialization in weakly supervised multi-label learning, an area that seeks to develop intelligent systems capable of learning from limited or incomplete labeled data. This field is crucial for reducing the cost and effort associated with data annotation, making AI more accessible and scalable. In addition, Dr. Fan is extensively involved in super-resolution image reconstruction, aiming to enhance the resolution of images using deep learning techniques and novel optimization models. His recent work on MDiffSR integrates mutual information and diffusion modeling to boost image quality and reconstruction accuracy, addressing core challenges in medical imaging, remote sensing, and surveillance. He also maintains active interest in areas such as representation learning, information theory in AI, and unsupervised learning strategies. His goal is to bridge the gap between theoretical development and real-world application, and his projects frequently target practical outcomes in health diagnostics, smart cities, and digital imaging. Dr. Fan’s interdisciplinary vision and technical versatility position him at the forefront of research aimed at making AI models more robust, efficient, and interpretable.

Research Skills

Assoc. Prof. Dr. Jinfu Fan possesses a comprehensive suite of research skills in both theoretical and applied computer science. His primary strength lies in machine learning algorithm design, where he has developed and evaluated models for classification, multi-label learning, and super-resolution tasks. He is proficient in deep learning frameworks such as TensorFlow and PyTorch, which he uses to build and test neural networks tailored for image processing and representation learning. Additionally, Dr. Fan has a strong grasp of information theory, particularly mutual information, which he applies to improve learning efficiency and performance in weakly supervised environments. His research methodology is firmly rooted in rigorous mathematical modeling and statistical analysis, supported by tools like Python, MATLAB, and R. He is also skilled in experimental design and evaluation, ensuring that his studies follow reproducible and scalable processes. Moreover, his experience with image reconstruction techniques, especially in the context of the MDiffSR project, demonstrates his ability to integrate domain-specific knowledge with advanced AI models. Dr. Fan’s collaborative experience at the National University of Singapore further highlights his international research exposure and ability to work on multidisciplinary teams, equipping him with both technical and cross-cultural collaboration skills vital for modern scientific research.

Awards and Honors

While specific awards and honors are not detailed in the available records, Assoc. Prof. Dr. Jinfu Fan’s academic and research career reflects commendable achievements that position him as a strong candidate for national and international recognition. His selection as a visiting scholar at the National University of Singapore (NUS)—a prestigious institution ranked among the top in Asia for computer science—demonstrates peer recognition of his academic potential and research capabilities. His publication in Neurocomputing, a well-regarded SCI-indexed journal, adds to his credentials, showcasing his ability to contribute impactful research to the scientific community. Dr. Fan’s research project “MDiffSR: Mutual information and diffusion model in image super-resolution” represents an innovative stride in AI-based imaging solutions and has earned citation attention, indicating its influence in the academic domain. As his body of work continues to expand, it is likely that his contributions will be recognized by leading societies and research funding bodies. He is an ideal candidate for honors such as the Best Researcher Award, Excellence in Research, or Outstanding Scientist Award, given his growing research portfolio, international collaborations, and commitment to academic advancement in machine learning and computer vision.

Author Metrics

  • Total Citations: 186

  • h-index: 8
    (8 publications have at least 8 citations each)

  • i10-index: 8
    (8 publications have at least 10 citations each)

Top Noted Publication

  • A dynamic ensemble method for residential short-term load forecasting
    Author: sY. Yang, F. Jinfu, W. Zhongjie, Z. Zheng, X. Yukun
    Journal: Alexandria Engineering Journal, 2023, 
    Citations: 25

  • GraphDPI: Partial label disambiguation by graph representation learning via mutual information maximization
    Author: J. Fan, Y. Yu, L. Huang, Z. Wang
    Journal: Pattern Recognition, 2023, 
    Citations: 23

  • Spatial-frequency dual-domain feature fusion network for low-light remote sensing image enhancement
    Author: Z. Yao, G. Fan, J. Fan, M. Gan, C.L.P. Chen
    Journal: IEEE Transactions on Geoscience and Remote Sensing, 2024.
    Citations: 17

  • A new multi-source transfer learning method based on two-stage weighted fusion
    Author: L. Huang, J. Fan, W. Zhao, Y. You
    Journal: Knowledge-Based Systems, 2023, 
    Citations: 17

  • Partial label learning based on disambiguation correction net with graph representation
    Author: J. Fan, Y. Yu, Z. Wang, J. Gu
    Journal: IEEE Transactions on Circuits and Systems for Video Technology, 2021, 
    Citations: 17

Conclusion

Assoc. Prof. Dr. Jinfu Fan exemplifies a forward-thinking academic who integrates rigorous research with practical innovation in the fields of machine learning and computer vision. With a solid educational foundation from Tongji University and global exposure through his time at the National University of Singapore, he brings a well-rounded and internationally informed perspective to his work. His specialized interests in weakly supervised multi-label learning and image super-resolution position him at the cutting edge of artificial intelligence research. The MDiffSR project, as one of his leading contributions, reflects his ability to blend theory with impactful applications. At Qingdao University, he not only leads significant research initiatives but also plays a vital role in mentoring students and fostering academic excellence. His research skills, encompassing deep learning, data mining, and mathematical modeling, make him a valuable contributor to both academic and industry-oriented projects. As he continues to expand his publication record and research collaborations, Dr. Fan stands as a promising candidate for recognition in scientific innovation. His commitment to knowledge advancement, problem-solving, and global engagement makes him a distinguished figure in the AI research landscape, with continued potential to make meaningful contributions to science and society.