Dr Jian Zhao | Image Processing | Best Researcher Award

Lecturer at Nanjing Institute of Technology, China

Dr. Jian Zhao is a dedicated lecturer at the School of Computer Engineering, Nanjing Institute of Technology, with a strong academic and research background in physical electronics and stereoscopic vision. He earned his doctoral degree from Southeast University and furthered his studies as a visiting student at Newcastle University, UK. Dr. Zhao has contributed significantly to the fields of micro-expression analysis, ultrafast phase-type spatial light modulation, and near-eye light field imaging. His work is supported by prestigious grants, including the National Natural Science Foundation of China and the Natural Science Foundation of Jiangsu Province. Dr. Zhao has published numerous high-impact papers in renowned journals, showcasing his expertise in light field displays, autostereoscopic displays, and computational imaging. His research continues to push the boundaries of visual display technologies and human-computer interaction.

Profile

Orcid

Education 🎓

Dr. Jian Zhao completed his doctoral studies in Physical Electronics at Southeast University from 2012 to 2019. During this period, he also spent a year as a visiting student at Newcastle University, UK, specializing in Stereoscopic Vision. His academic journey reflects a strong foundation in electronic science and engineering, complemented by international exposure. Dr. Zhao’s postdoctoral work at Southeast University further solidified his expertise in advanced display technologies and computational imaging. His educational background has equipped him with the skills to lead innovative research projects and contribute to the academic community through teaching and mentorship.

Experience 💼

Dr. Jian Zhao has extensive research and academic experience, currently serving as a lecturer at Nanjing Institute of Technology since 2019. He has led multiple research projects funded by the National Natural Science Foundation of China and the Jiangsu Provincial Department of Education. His work focuses on micro-expression analysis, ultrafast spatial light modulation, and near-eye light field displays. Dr. Zhao has also collaborated on interdisciplinary projects, such as mobile phone screen scratch detection and urban waterlogging monitoring using deep learning. His contributions to the field of photonics and display technologies have been widely recognized, making him a respected figure in academia.

Awards and Honors 🏆

Dr. Jian Zhao has received several accolades for his contributions to research and academia. His projects have been funded by prestigious grants, including the National Natural Science Foundation of China and the Natural Science Foundation of Jiangsu Province. Dr. Zhao’s work on near-eye light field displays and autostereoscopic displays has been published in high-impact journals, earning him recognition in the field of photonics and computational imaging. His innovative approaches to visual display technologies and human-computer interaction continue to garner attention and acclaim within the scientific community.

Research Focus 🔍

Dr. Jian Zhao’s research focuses on advanced display technologies, including near-eye light field displays, autostereoscopic displays, and ultrafast spatial light modulation. He explores the intersection of human visual perception and computational imaging, aiming to enhance user experiences in virtual and augmented reality. His work also delves into micro-expression analysis in complex environments and the application of deep learning for image processing tasks, such as mobile phone screen scratch detection and urban waterlogging monitoring. Dr. Zhao’s interdisciplinary approach bridges the gap between theoretical research and practical applications, driving innovation in the field of photonics and display technologies.

Publication Top Notes 📚

Spatial loss factor for the analysis of accommodation depth cue on near-eye light field displays – Optics Express, 2019
Hybrid Computational Near-Eye Light Field Display – IEEE Photonics Journal, 2019
Viewing Zone Expansion of Autostereoscopic Display With Composite Lenticular Lens Array and Saddle Lens Array – IEEE Photonics Journal, 2023
Mobile phone screen surface scratch detection based on the Optimized YOLOv5 model (OYm) – IET Image Processing, 2022
Advances in Virtual Viewpoint Generation Technology for Light Field Display – Chinese Journal of Liquid Crystals and Displays, 2023
Urban Waterlogging Monitoring and Recognition in Low-Light Scenarios Using Surveillance Videos and Deep Learning – Water, 2025
A multimodal visual fatigue assessment model based on back propagation neural network and XGBoost – Displays, 2024
Study on Random Generation of Virtual Avatars Based on Big Data – Communications in Computer and Information Science, 2023
Switchable Photonic Nanojet by Electro-Switching Nematic Liquid Crystals – Nanomaterials, 2019

Conclusion 🌟

Dr. Jian Zhao is a distinguished researcher and educator whose work in advanced display technologies and computational imaging has made significant contributions to the field of photonics. His innovative research, supported by prestigious grants and published in high-impact journals, reflects his commitment to pushing the boundaries of visual display technologies. Dr. Zhao’s interdisciplinary approach and dedication to both teaching and research make him a valuable asset to the academic community. His ongoing projects and publications continue to inspire advancements in human-computer interaction and visual perception.

Dr. Xin Yuan | Computer Vision | Best Researcher Award

Dr. Xin Yuan | Computer Vision – Wuhan University of Science and Technology, China

Xin Yuan is a dedicated researcher in computer vision and artificial intelligence, specializing in object re-identification, image retrieval, and deep metric learning. His work is at the intersection of theory, algorithm development, and real-world applications, making significant contributions to visual recognition and deep learning advancements. With a strong academic foundation and an extensive publication record, he has demonstrated an exceptional ability to develop novel methodologies that improve the accuracy and efficiency of image retrieval and object recognition systems. His contributions have been recognized with multiple awards, reflecting his commitment to advancing the field and shaping the future of artificial intelligence-driven image analysis.

Professional Profile

Google Scholar | ORCID

Education

Xin Yuan pursued his Bachelor of Engineering in Computer Science and Technology at Wuhan University of Science and Technology, where he laid the groundwork for his expertise in artificial intelligence and deep learning. His passion for research led him to continue at the same institution, earning a Ph.D. in Control Science and Engineering. Throughout his academic journey, he exhibited remarkable research capabilities, earning distinctions such as the Outstanding Graduate award. His doctoral research provided critical insights into optimizing deep learning models for person re-identification and image retrieval, enhancing the robustness and scalability of these technologies.

Experience

Currently serving as a lecturer at the School of Computer Science and Technology at Wuhan University of Science and Technology, Xin Yuan plays an instrumental role in both academia and research. His expertise has been sought after for numerous high-profile conferences and peer-reviewed journals, where he serves as a reviewer and committee member. His experience extends beyond theoretical research, as he actively collaborates with industry leaders and fellow researchers to implement state-of-the-art artificial intelligence solutions. His professional engagements include serving on organizing committees for prestigious conferences, highlighting his influence in the global research community.

Research Interest

Xin Yuan’s research primarily focuses on object re-identification, image retrieval, and deep metric learning. His theoretical work involves analyzing and improving the generalization ability of loss functions, ensuring deep learning models can perform effectively across various domains. Algorithmically, he develops novel deep learning architectures to enhance the accuracy and efficiency of person re-identification and image retrieval tasks. His applied research translates these advancements into real-world scenarios, where AI-driven solutions can significantly improve security, surveillance, and intelligent image processing. By bridging theory and application, he continues to push the boundaries of what AI can achieve in the realm of visual recognition.

Awards and Honors

Throughout his career, Xin Yuan has received numerous accolades in recognition of his outstanding research contributions. His achievements include the Best Researcher Award (2025), acknowledging his exceptional work in artificial intelligence and computer vision. Additionally, he has been honored with the Hubei Youth May Fourth Medal (2023) and the Baosteel Outstanding Student Award (2022) for his academic excellence and innovative contributions. His success in national and international competitions further showcases his dedication to advancing scientific knowledge and making a lasting impact on the research community. These awards are a testament to his unwavering commitment to excellence and his role as a leading figure in AI research.

Publications

Identity Hides in Darkness: Learning Feature Discovery Transformer for Nighttime Person Re-identification – Sensors, 2025 📷

VAGeo: View-specific Attention for Cross-View Object Geo-Localization – ICASSP’25, 2025 🛰️

Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration – ICASSP’25, 2025 🎥

Mix-Modality Person Re-Identification: A New and Practical Paradigm – ACM T-MM, 2025 🔍

Spatial Bi-Exploration for Robust Camouflaged Object Detection – IEEE Signal Processing Letters, 2025 🦎

RLUNet: Overexposure-Content-Recovery-Based Single HDR Image Reconstruction – Applied Sciences, 2024 🌅

Blind 3D Video Stabilization with Spatio-Temporally Varying Motion Blur – The Visual Computer, 2024 🎬

Conclusion

Xin Yuan’s contributions to computer vision and artificial intelligence exemplify his dedication to advancing knowledge and solving complex challenges in the field. His research has significantly impacted object re-identification, image retrieval, and deep metric learning, paving the way for innovative AI-driven solutions. His extensive academic background, research excellence, and numerous accolades make him a deserving candidate for the Best Researcher Award. With a strong foundation in both theoretical and applied research, he continues to inspire and lead in the scientific community, pushing the frontiers of deep learning and artificial intelligence. His future endeavors promise even greater contributions, further solidifying his status as a pioneering researcher in AI and computer vision.

Jian Zhao | Image Processing | Best Researcher Award